The spread of coronavirus (COVID-19) in Italy has hit every age group uniformly and claimed over 190 thousand lives since it entered the country. As the chart shows, however, mortality rate appeared to be much higher for the elderly patient. In fact, for people between 80 and 89 years of age, the fatality rate was 6.1 percent. For patients older than 90 years, this figure increased to 12.1 percent. On the other hand, the death rate for individuals under 60 years of age was well below 0.5 percent. Overall, the mortality rate of coronavirus in Italy was 0.7 percent.

Italy's death toll was one of the most tragic in the world. In the last months, however, the country started to see the end of this terrible situation: as of May 2023, roughly 84.7 percent of the total Italian population was fully vaccinated.

Since the first case was detected at the end of January in Italy, coronavirus has been spreading fast. As of May, 2023, the authorities reported over 25.8 million cases in the country. The area mostly hit by the virus is the North, in particular the region of Lombardy.

For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

After entering Italy, coronavirus (COVID-19) has been spreading fast. An analysis of the individuals who died after contracting the virus revealed that the vast majority of deaths occurred among the elderly. As of May, 2023, roughly 85 percent were patients aged 70 years and older.

Italy's death toll was one of the most tragic in the world. In the last months, however, the country saw the end to this terrible situation: as of May 2023, roughly 84.7 percent of the total Italian population was fully vaccinated.

As of May, 2023, the total number of cases reported in the country were over 25.8 million. The North of the country was the mostly hit area, and the region with the highest number of cases was Lombardy.

For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

After the outbreak of the coronavirus (COVID-19) in Italy, many people died after contracting the infection. As of May 2023, the mortality rate for female patients in Italy was 0.6 percent, the corresponding figure for male patients was 0.9 percent. The chart shows how this gap was recorded among all age groups.

Italy's death toll was one of the most tragic in the world. In the last months, however, the country saw the end to this terrible situation: as of May 2023, roughly 84.7 percent of the total Italian population was fully vaccinated.

The virus originated in Wuhan, a Chinese city populated by millions and located in the province of Hubei. More statistics and facts about the virus in Italy are available here. For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

The spread of coronavirus (COVID-19) in Italy has not hit uniformly people of every age, as about 60 percent of the individuals infected with the virus were under 50 years old. However, deaths occurred mostly among the elderly. The virus has claimed approximately 190 thousand lives, but, as the chart shows, roughly 85 percent of the victims were older people, aged 70 years or more. People between 80 and 89 years were the most affected, with roughly 76 thousand deaths within this age group.

Number of total cases Since the first case was detected, coronavirus has spread quickly across Italy. As of April 2023, authorities have reported over 25.8 million cases in the country. This figure includes the deceased, the recovered, and current active cases. COVID recoveries represent the vast majority, reaching approximately 25.5 million.

Regional differences In terms of COVID cases, Lombardy has been the hardest hit region, followed by the regions of Campania, and Veneto. Likewise, in terms of deaths, Lombardy was the region with the highest number, with roughly 46 thousand losses. On the other hand, this is also the region with the highest number of COVID-19 vaccine administered doses, with a figure of approximately 25.5 million.

For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

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.

Over the course of 2020, 75,891 deaths caused by COVID-19 were reported to the authorities in Italy. In total, the number of deaths in the country surpassed 746 thousand, the highest figure registered since World War II. This statistic shows the percentage change in the number of deaths per age group of the individuals who died, comparing figures for 2020 with the average of deaths in the same period between 2015 and 2019. The three periods considered correspond to three main stages of 2020 in Italy: the pre-COVID-19 months, the first wave, and the second wave. It is possible to see how COVID-19 impacted the different age groups disproportionately. The number of deaths recorded among individuals between zero and 49 years old, in fact, was even consistently less than the 2015-2019 average across 2020. On the other hand, during the first and second wave of infections, the number of deaths registered among people aged 80 years or more was 36 percent higher than the 2015-2019 average.

Abstract

COVID-19 integrated surveillance data provided by the Italian National Institute of Health and processed via UnrollingAverages.jl to deconvolve the weekly simple moving averages.

Overview

Every week the National Institute for Nuclear Physics (INFN) imports an anonymous individual-level dataset from the Italian National Institute of Health (ISS) and converts it into an incidence time series data organized by date of event and disaggregated by sex, age and administrative level with a consolidation period of approximately two weeks. The information available to the INFN is summarised in the following meta-table.

Output Data

The output data has been stored here and contain the following information:

Reconstructed daily time series of confirmed cases by date of diagnosis stratified by sex and age at the regional level;

Reconstructed daily time series of symptomatic cases by date of symptoms onset stratified by sex and age at the regional level;

Reconstructed daily time series of ordinary hospital admissions by date of admission stratified by sex and age at the regional level;

Reconstructed daily time series of intensive hospital admissions by date of admission stratified by sex and age at the regional level;

Reconstructed daily time series of deceased cases by date of death stratified by sex and age at the regional level.

Background and aimPatients with interstitial lung diseases, including asbestosis, showed high susceptibility to the SARS-CoV-2 virus and a high risk of severe COVID-19 symptoms. Italy, highly impacted by asbestos-related diseases, in 2020 was among the European countries with the highest number of COVID-19 cases. The mortality related to malignant mesotheliomas and asbestosis in 2020 and its relationship with COVID-19 in Italy are investigated.MethodsAll death certificates involving malignant mesotheliomas or asbestosis in 2010–2020 and those involving COVID-19 in 2020 were retrieved from the National Registry of Causes of Death. Annual mortality rates and rate ratios (RRs) of 2020 and 2010–2014 compared to 2015–2019 were calculated. The association between malignant pleural mesothelioma (MPM) and asbestosis with COVID-19 in deceased adults ≥80 years old was evaluated through a logistic regression analysis (odds ratios: ORs), using MPM and asbestosis deaths COVID-19-free as the reference group. The hospitalization for asbestosis in 2010–2020, based on National Hospital Discharge Database, was analyzed.ResultsIn 2020, 746,343 people died; out of them, 1,348 involved MPM and 286 involved asbestosis. Compared to the period 2015–2019, the mortality involving the two diseases decreased in age groups below 80 years; meanwhile, an increasing trend was observed in subjects aged 80 years and older, with a relative mortality risks of 1.10 for MPM and 1.17 for asbestosis. In subjects aged ≥80 years, deaths with COVID-19 were less likely to have MPM in both genders (men: OR = 0.22; women: OR = 0.44), while no departure was observed for asbestosis. A decrease in hospitalization in 2020 with respect to those in 2010–2019 in all age groups, both considering asbestosis as the primary or secondary diagnosis, was observed.ConclusionsThe increasing mortality involving asbestosis and, even if of slight entity, MPM, observed in people aged over 80 years during the 1st year of the COVID-19 pandemic, aligned in part with the previous temporal trend, could be due to several factors. Although no positive association with COVID-19 mortality was observed, the decrease in hospitalizations for asbestosis among individuals aged over 80 years, coupled with the increase in deaths, highlights the importance of enhancing home-based assistance during the pandemic periods for vulnerable patients with asbestos-related conditions.

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....

Background: Italy has one of the world's oldest populations, and suffered one the highest death tolls from Coronavirus disease 2019 (COVID-19) worldwide. Older people with cardiovascular diseases (CVDs), and in particular hypertension, are at higher risk of hospitalization and death for COVID-19. Whether hypertension medications may increase the risk for death in older COVID 19 inpatients at the highest risk for the disease is currently unknown.Methods: Data from 5,625 COVID-19 inpatients were manually extracted from medical charts from 61 hospitals across Italy. From the initial 5,625 patients, 3,179 were included in the study as they were either discharged or deceased at the time of the data analysis. Primary outcome was inpatient death or recovery. Mixed effects logistic regression models were adjusted for sex, age, and number of comorbidities, with a random effect for site.Results: A large proportion of participating inpatients were ≥65 years old (58%), male (68%), non-smokers (93%) with comorbidities (66%). Each additional comorbidity increased the risk of death by 35% [adjOR = 1.35 (1.2, 1.5) p < 0.001]. Use of ACE inhibitors, ARBs, beta-blockers or Ca-antagonists was not associated with significantly increased risk of death. There was a marginal negative association between ARB use and death, and a marginal positive association between diuretic use and death.Conclusions: This Italian nationwide observational study of COVID-19 inpatients, the majority of which ≥65 years old, indicates that there is a linear direct relationship between the number of comorbidities and the risk of death. Among CVDs, hypertension and pre-existing cardiomyopathy were significantly associated with risk of death. The use of hypertension medications reported to be safe in younger cohorts, do not contribute significantly to increased COVID-19 related deaths in an older population that suffered one of the highest death tolls worldwide.

Objective: This study examined cumulative excess mortality in European countries in the year of the Covid-19 pandemic and characterized the dynamics of the pandemic in different countries, focusing on Hungary and the Central and Eastern European region.Methods: Age-standardized cumulative excess mortality was calculated based on weekly mortality data from the EUROSTAT database, and was compared between 2020 and the 2016–2019 reference period in European countries.Results: Cumulate weekly excess mortality in Hungary was in the negative range until week 44. By week 52, it reached 9,998 excess deaths, corresponding to 7.73% cumulative excess mortality vs. 2016–2019 (p-value = 0.030 vs. 2016–2019). In Q1, only Spain and Italy reported excess mortality compared to the reference period. Significant increases in excess mortality were detected between weeks 13 and 26 in Spain, United Kingdom, Belgium, Netherland and Sweden. Romania and Portugal showed the largest increases in age-standardized cumulative excess mortality in the Q3. The majority of Central and Eastern European countries experienced an outstandingly high impact of the pandemic in Q4 in terms of excess deaths. Hungary ranked 11th in cumulative excess mortality based on the latest available data of from the EUROSTAT database.Conclusion: Hungary experienced a mortality deficit in the first half of 2020 compared to previous years, which was followed by an increase in mortality during the second wave of the COVID-19 pandemic, reaching 7.7% cumulative excess mortality by the end of 2020. The excess was lower than in neighboring countries with similar dynamics of the pandemic.

Background and aims There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. Methods and results Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6-14.7 for age ?85 vs 18-44 y); HR = 4.7; 2.9-7.7 for estimated glomerular filtration rate levels <15 vs ? 90 mL/min/1.73 m2; HR = 2.3; 1.5-3.6 for C-reactive protein levels ?10 vs ? 3 mg/L). No relation was found with obesity, tobacco use, cardiovascular disease and related-comorbidities. The associations between these variables and mortality were substantially homogenous across all sub-groups analyses. Conclusions Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy.

Since the beginning of the 2000s, the number of deaths in Italy remained rather stable. In 2020, on the contrary, the death rate reached 12.5 per 1,000 inhabitants, a notable increase compared to previous years. Four years after the pandemic, the figure remains above 10 deaths per 1,000 residents. From the perspective of the single regions, the highest number of deaths was registered in Liguria, whereas the lowest death rate in the country was reported in Trentino-Alto Adige. Coronavirus in Italy In Italy, the first cases of coronavirus (COVID-19) were registered at the end of January 2020. Then, since the end of February, the virus started to spread among the Italian population. Data on the infected patients show that COVID-19 has hit every age group uniformly, but the mortality rate appears to be much higher for elderly patients. Death rates in Europe Despite being the fourth-largest country in Europe in terms of population size, Italy was the state with the second-highest number of deaths, preceded only by Germany, the most populated country on the continent.

Context

Amidst the COVID-19 outbreak, the world is facing great crisis in every way. The value and things we built as a human race are going through tremendous challenges. It is a very small effort to bring curated data set on Novel Corona Virus to accelerate the forecasting and analytical experiments to cope up with this critical situation. It will help to visualize the country level out break and to keep track on regularly added new incidents.

COVID-19 Country Level Timeseries Dataset

This Dataset contains country wise public domain time series information on COVID-19 outbreak. The Data is sorted alphabetically on Country name and Date of Observation.

Column Descriptions

The data set contains the following columns:
ObservationDate: The date on which the incidents are observed country: Country of the Outbreak Confirmed: Number of confirmed cases till observation date Deaths: Number of death cases till observation date Recovered: Number of recovered cases till observation date New Confirmed: Number of new confirmed cases on observation date New Deaths: Number of New death cases on observation date New Recovered: Number of New recovered cases on observation date latitude: Latitude of the affected country longitude: Longitude of the affected country

Acknowledgements

This data set is a cleaner version of the https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset data set with added geo location information and regularly added incident counts. I would like to thank this great effort by SRK.

Original Data Source

Johns Hopkins University 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

Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic period. We compared 6 calculations of excess deaths (4 previously published and two new ones that we performed with and without age-adjustment) for 2020-2021. With each approach, we calculated excess deaths metrics and the ratio R of excess deaths over recorded COVID-19 deaths. The main analysis focused on 33 high-income countries with weekly deaths in the Human Mortality Database (HMD at mortality.org) and reliable death registration. Secondary analyses compared calculations for other countries, whenever available. Across the 33 high-income countries, excess deaths were 2.0-2.8 million without age-adjustment, and 1.6-2.1 million with age-adjustment with large differences across countries. In our analyses after age-adjustment, 8 of 33 countries had no overall excess deaths; there was a death deficit in children; and 0.478 million (29.7%) of the excess deaths were in people <65 years old. In countries like France, Germany, Italy, and Spain excess death estimates differed 2 to 4-fold between highest and lowest figures. The R values’ range exceeded 0.3 in all 33 countries. In 16 of 33 countries, the range of R exceeded 1. In 25 of 33 countries some calculations suggest R>1 (excess deaths exceeding COVID-19 deaths) while others suggest R<1 (excess deaths smaller than COVID-19 deaths). Inferred data from 4 evaluations for 42 countries and from 3 evaluations for another 98 countries are very tenuous Estimates of excess deaths are analysis-dependent and age-adjustment is important to consider. Excess deaths may be lower than previously calculated.

Inspiration

Which countries have the most social contacts in the world? In particular, do countries with more social contacts among the elderly report more deaths caused by a pandemic caused by a respiratory virus?

Context

With the emergence of the COVID-19 pandemic, reports have shown that the elderly are at a higher risk of dying than any other age groups. 8 out of 10 deaths reported in the U.S. have been in adults 65 years old and older. Countries have also began to enforce 2km social distancing to contain the pandemic.

To this end, I wanted to explore the relationship between social contacts among the elderly and its relationship with the number of COVID-19 deaths across countries.

Content

This dataset includes a subset of the projected social contact matrices in 152 countries from surveys Prem et al. 2020. It was based on the POLYMOD study where information on social contacts was obtained using cross-sectional surveys in Belgium (BE), Germany (DE), Finland (FI), Great Britain (GB), Italy (IT), Luxembourg (LU), The Netherlands (NL), and Poland (PL) between May 2005 and September 2006.

This dataset includes contact rates from study participants ages 65+ for all countries from all sources of contact (work, home, school and others).

I used this R code to extract this data:

load('../input/contacts.Rdata') # https://github.com/kieshaprem/covid19-agestructureSEIR-wuhan-social-distancing/blob/master/data/contacts.Rdata
View(contacts)
contacts[["ALB"]][["home"]]
contacts[["ITA"]][["all"]]
rowSums(contacts[["ALB"]][["all"]])
out1 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[16,]; out <- rbind(out, data.frame(x)) }
out2 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[15,]; out <- rbind(out, data.frame(x)) }
out3 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[14,]; out <- rbind(out, data.frame(x)) }
m1 = data.frame(t(matrix(unlist(out1), nrow=16)))
m2 = data.frame(t(matrix(unlist(out2), nrow=16)))
m3 = data.frame(t(matrix(unlist(out3), nrow=16)))
rownames(m1) = names(contacts)
colnames(m1) = c("00_04", "05_09", "10_14", "15_19", "20_24", "25_29", "30_34", "35_39", "40_44", "45_49", "50_54", "55_59", "60_64", "65_69", "70_74", "75_79")
rownames(m2) = rownames(m1)
rownames(m3) = rownames(m1)
colnames(m2) = colnames(m1)
colnames(m3) = colnames(m1)
write.csv(zapsmall(m1),"contacts_75_79.csv", row.names = TRUE)
write.csv(zapsmall(m2),"contacts_70_74.csv", row.names = TRUE)
write.csv(zapsmall(m3),"contacts_65_69.csv", row.names = TRUE)

Rows names correspond to the 3 letter country ISO code, e.g. ITA represents Italy. Column names are the age groups of the individuals contacted in 5 year intervals from 0 to 80 years old. Cell values are the projected mean social contact rate.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1139998%2Ffa3ddc065ea46009e345f24ab0d905d2%2Fcontact_distribution.png?generation=1588258740223812&alt=media" alt="">

Acknowledgements

Thanks goes to Dr. Kiesha Prem for her correspondence and her team for publishing their work on social contact matrices.

References

• The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study
• Projecting social contact matrices in 152 countries using contact surveys and demographic data
• Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases (POLYMOD study)

Related resources

• My starter notebook
• http://www.socialcontactdata.org/
• https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan
• Facebook Data for Good Mobility Dashboard

This record contains raw data related to article “An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study"

Abstract:

Introduction: Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning.

Material and methods: We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation.

Results: 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality.

Conclusions: Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

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, in the following months the country had to face four new harsh waves of contagion. As of January 1, 2025, 198,638 deaths caused by COVID-19 were reported by the authorities, of which approximately 48.7 thousand in the region of Lombardy, 20.1 thousand in the region of Emilia-Romagna, and roughly 17.6 thousand in Veneto, the regions mostly hit. The total number of cases reported in the country 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 counted about 2.9 million cases. Italy's death toll was one of the most tragic in the world. In the last months, however, the country saw the end to this terrible situation: as of November 2023, 85 percent of the total Italian population was fully vaccinated. For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

Background: Severe pneumonia is pathological manifestation of Coronavirus Disease 2019 (COVID-19), however complications have been reported in COVID-19 patients with a worst prognosis. Aim of this study was to evaluate the role of high sensitivity cardiac troponin I (hs-TnI) in patients with SARS-CoV-2 infection.

Methods: we retrospectively analysed hs-TnI values measured in 523 patients (median age 64 years, 68% men) admitted to a university hospital in Milan, Italy, and diagnosed COVID-19.

Results: A significant difference in hs-TnI concentrations was found between deceased patients (98 patients) vs discharged (425 patients) [36.05 ng/L IQR 16.5-94.9 vs 6.3 ng/L IQR 2.6-13.9, p < 0.001 respectively]. Hs-TnI measurements were independent predictors of mortality at multivariate analysis adjusted for confounding parameters such as age (HR 1.004 for each 10 point of troponin, 95% CI 1.002-1.006, p < 0.001). The survival rate, after one week, in patients with hs-TnI values under 6 ng/L was 97.94%, between 6 ng/L and the normal value was 90.87%, between the normal value and 40 ng/L was 86.98, and 59.27% over 40 ng/L.

Conclusion: Increase of hs-TnI associated with elevated mortality in patients with COVID-19. Troponin shows to be a useful biomarker of disease progression and worse prognosis in COVID-19 patients.

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...