Italy recorded 25828252 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Italy reported 190080 Coronavirus Deaths. This dataset includes a chart with historical data for Italy Coronavirus Cases.

Context

Due to the coronavirus epidemic, and following the measures adopted by the Government to contain it, Istat has implemented a series of actions to ensure the continuity and quality of statistical production even in the emergency situation.

Content

TheThe Italian National Institute of Statistics has reorganized data collection by sustainable acquisition techniques, innovative methodologies and use of data sources; it also provided most appropriate solutions to support statistical production processes, in full protection of workers’ health.

Official statistics are fundamental for measuring the evolution of economy and society; their production and dissemination at the service of institutions, policy-makers, families and businesses, therefore, cannot be stopped, but need to be rethought to be ready to provide the country with all necessary answers, and above all to support and monitor the future country’s recovery.

Reference period: 01/01-04/04, Years 2015-2020. Collected data thanks to Istat Survey Deaths of resident population, that uses administrative source to collect main individual characteristics of deaths, and to processing ANPR (National Resident Population Register) source data for deaths referring to the 2020 year.

Processing data of municipalities (1,689) where ANPR data are considered reliable and migrated in ANPR database before January 1st, 2020.

Record: 1. CODES NUTS2 = Istat code of NUTS2
2. CODES NUTS3 = Istat code of NUTS3 3. CODES_NUTS3_LAU2 = Istat code of LAU2 4. NUTS 2 = Region of residence 5. NUTS 3 = Province of residence 6. LAU 2 = Municipality of residence 7. DATA_INIZIO_DIFF = Date of first dissemination of data in 2020 8. WEEK= Week of death (first considered period, from January 1st to January 11th, is 11 days) 9. AGE CLASS = Age class at the time of death 10. MALES_2015: total male deaths in 2015 11. MALES_2016: total male deaths in 2016 12. MALES_2017: total male deaths in 2017 13. MALES_2018: total male deaths in 2018 14. MALES_2019: total male deaths in 2019 15. MALES_2020: total male deaths in 2020 16. FEMALES_2015: total female deaths in 2015 17. FEMALES_2016: total female deaths in 2016 18. FEMALES_2017: total female deaths in 2017 19. FEMALES_2018: total female deaths in 2018 20. FEMALES_2019: total female deaths in 2019 21. FEMALES_2020: total female deaths in 2020 22. TOTAL_2015: total deaths in 2015 23. TOTAL_2016: total deaths in 2016 24. TOTAL_2017: total deaths in 2017 25. TOTAL_2018: total deaths in 2018 26. TOTAL_2019: total deaths in 2019 27. TOTAL_2020: total deaths in 2020

Acknowledgements

https://www.istat.it

https://www.istat.it/en/archivio/240106

Inspiration

These data also provides most appropriate solutions to support statistical production processes, in full protection of workers’ health.

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

We calculate excess mortality, i.e., attributable to Covid-19, across all (7,272) Italian municipalities using death registry data (see References) by deducting from a municipality's (daily) number of deaths the average number of deaths over the previous five years in the same municipality, using an evenly-spaced-around window of seven days. The date ("GE" column) is in "mmdd" format, while the municipality code ("codice_comune" column) follows standard ISTAT codes. The sample period runs from February to April 2020, and excludes municipalities that were never hit by the Covid-19 disease within the first four months of 2020, i.e. cumulative mortality rate among residents of a municipality did not reach a threshold of 100 deaths per 100,000 inhabitants.

AbstractIn Italy, approximately 400.000 new cases of malignant tumors are recorded every year. The average of annual deaths caused by tumors, according to the Italian Cancer Registers, is about 3.5 deaths and about 2.5 per 1,000 men and women respectively, for a total of about 3 deaths every 1,000 people. Long-term (at least a decade) and spatially detailed data (up to the municipality scale) are neither easily accessible nor fully available for public consultation by the citizens, scientists, research groups, and associations. Therefore, here we present a ten-year (2009–2018) database on cancer mortality rates (in the form of Standardized Mortality Ratios, SMR) for 23 cancer macro-types in Italy on municipal, provincial, and regional scales. We aim to make easily accessible a comprehensive, ready-to-use, and openly accessible source of data on the most updated status of cancer mortality in Italy for local and national stakeholders, researchers, and policymakers and to provide researchers with ready-to-use data to perform specific studies. Methods For a given locality, year, and cause of death, the SMR is the ratio between the observed number of deaths (Om) and the number of expected deaths (Em): SMR = Om/Em (1) where Om should be an available observational data and Em is estimated as the weighted sum of age-specific population size for the given locality (ni) per age-specific death rates of the reference population (MRi): Em = sum(MRi x ni) (2) MRi could be provided by a public health organization or be estimated as the ratio between the age-specific number of deaths of reference population (Mi) to the age-specific reference population size (Ni): MRi = Mi/Ni (3) Thus, the value of Em is weighted by the age distribution of deaths and population size. SMR assumes value 1 when the number of observed and expected deaths are equal. Following eqns. (1-3), the SMR was computed for single years of the period 2009-2018 and for single cause of death as defined by the International ICD-10 classification system by using the following data: age-specific number of deaths by cause of reference population (i.e., Mi) from the Italian National Institute of Statistics (ISTAT, (http://www.istat.it/en/, last access: 26/01/2022)); age-specific census data on reference population (i.e., Ni) from ISTAT; the observed number of deaths by cause (i.e., Om) from ISTAT; the age-specific census data on population (ni); the SMR was estimated at three different level of aggregation: municipal, provincial (equivalent to the European classification NUTS 3) and regional (i.e., NUTS2). The SMR was also computed for the broad category of malignant tumors (i.e. C00-C979, hereinafter cancer macro-type C), and for the broad category of malignant tumor plus non-malignant tumors (i.e. C00-C979 plus D0-D489, hereinafter cancer macro-type CD). Lower 90% and 95% confidence intervals of 10-year average values were computed according to the Byar method.

Italy IT: Death Rate: Crude: per 1000 People data was reported at 10.100 Ratio in 2016. This records a decrease from the previous number of 10.700 Ratio for 2015. Italy IT: Death Rate: Crude: per 1000 People data is updated yearly, averaging 9.800 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 10.700 Ratio in 2015 and a record low of 9.300 Ratio in 1961. Italy IT: Death Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Population and Urbanization Statistics. Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Dataset preparation and annotation

The dataset is a subset of the TBCOV dataset collected at QCRI filtered for mentions of personally related COVID-19 deaths. The filtering was done using regular expressions such as my * passed, my * died, my * succumbed & lost * battle. A sample of the dataset was annotated on Appen. Please see 'annotation-instructions.txt' for the full instructions provided to the annotators.

Dataset description

The "classifier_filtered_english.csv" file contains 33k deduplicated and classifier-filtered tweets (following X's content redistribution policy). for the 6 countries (Australia, Canada, India, Italy, United Kingdom, and United States) from March 2020 to March 2021 with classifier-labeled death labels, regular expression-filtered gender and relationship labels, and the user device label. The full 57k regex-filtered collection of tweets can be made available on special cases for Academics and Researchers.

date: the date of the tweet

country_name: the country name from Nominatim API

tweet_id: the ID of the tweet

url: the full URL of the tweet

full_text: the full-text content of the tweet (also includes the URL of any media attached)

does_the_tweet_refer_to_the_covidrelated_death_of_one_or_more_individuals_personally_known_to_the_tweets_author: the classifier predicted label for the death (also includes the original labels for the annotated samples)

what_is_the_relationship_between_the_tweets_author_and_the_victim_mentioned: the annotated relationship labels

relative_to_the_time_of_the_tweet_when_did_the_mentioned_death_occur: the annotated relative time labels

user_is_verified: if the user is verified or not

user_gender: the gender of the Twitter user (from the user profile)

user_device: the Twitter client the user uses

has_media: if the tweet has any attached media

has_url: if the tweet text contains a URL

matched_device: the device (Apple or Android) based on the Twitter client

regex_gender: the gender inferred from regular expression-based filtering

regex_relationship: the relationship label from regular expression-based filtering

Inferring gender using regular expressions

We first determine the mapping between different relationship labels mentioned in the tweet to the gender. We do not use any relationship like "cousin" from which we cannot easily infer the gender.

Male relationships: 'father', 'dad', 'daddy', 'papa', 'pop', 'pa', 'son', 'brother', 'uncle', 'nephew', 'grandfather', 'grandpa', 'gramps', 'husband', 'boyfriend', 'fiancé', 'groom', 'partner', 'beau', 'friend', 'buddy', 'pal', 'mate', 'companion', 'boy', 'gentleman', 'man', 'father-in-law', 'brother-in-law', 'stepfather', 'stepbrother'

Female relationships: 'mother', 'mom', 'mama', 'mum', 'ma', 'daughter', 'sister', 'aunt', 'niece', 'grandmother', 'grandma', 'granny', 'wife', 'girlfriend', 'fiancée', 'bride', 'partner', 'girl', 'lady', 'woman', 'miss', 'mother-in-law', 'sister-in-law', 'stepmother', 'stepsister'

Based on these mappings, we used the following regex for each gender label to determine the gender of the deceased mentioned in the tweet.

"[m|M]y\s(" + "|".join([r + "s?" for r in relationships]) + ")\s(died|succumbed|deceased)"

Age groups from relationship labels

First, we get the relationship labels using regex filtering, and then we group them into different age-group categories as shown in the following table. The UK and the US use different age groups because of the different age group definitions in the official data.

Training the classifier

The 'english-training.csv' file contains about 13k deduplicated human-annotated tweets. We use a random seed (42) to create the train/test split. The model Covid-Bert-V2 was fine-tuned on the training set for 2 epochs with the following hyperparameters (obtained using 10-fold CV): random_seed: 42, batch_size: 32, dropout: 0.1. We obtained a F1-score of 0.81 on the test set. We used about 5% (671) of the combined and deduplicated annotated tweets as the test set, about 2% (255) as the validation set, and the remaining 12,494 tweets were used for fine-tuning the model. The tweets were preprocessed to replace mentions, URLs, emojis, etc with generic keywords. The model was trained on a system with a single Nvidia A4000 16GB GPU. The fine-tuned model is also available as the 'model.bin' file. The code for finetuning the model as well as reproducing the experiments are available in this GitHub repository.

Datasheet

We also include a datasheet for the dataset following the recommendation of "Datasheets for Datasets" (Gebru et. al.) which provides more information about how the dataset was created and how it can be used. Please see "Datasheet.pdf".

NOTE: We recommend that researchers try to rehydrate the individual tweets to ensure that the user has not deleted the tweet since posting. This gives users a mechanism to opt out of having their data analyzed.

Please only use your institutional email when requesting the dataset as anything else (like gmail.com) will be rejected. The dataset will only be made available on reasonable request for Academics and Researchers. Please mention why you need the dataset and how you plan to use the dataset when making a request.

Italy IT: Number of Deaths Ages 15-19 Years data was reported at 616.000 Person in 2019. This records an increase from the previous number of 614.000 Person for 2018. Italy IT: Number of Deaths Ages 15-19 Years data is updated yearly, averaging 1,063.000 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 2,384.000 Person in 1990 and a record low of 597.000 Person in 2016. Italy IT: Number of Deaths Ages 15-19 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Health Statistics. Number of deaths of adolescents ages 15-19 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Context

The 2019–20 coronavirus pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, Hubei, China, in December 2019. On 11 March 2020, the World Health Organization declared the outbreak a pandemic. As of 11 March 2020, over 126,000 cases have been confirmed in more than 110 countries and territories, with major outbreaks in mainland China, Italy, South Korea, and Iran. More than 4,600 have died from the disease and 67,000 have recovered.

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 information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this data was scrapped from https://www.worldometers.info/coronavirus/.This data is solely for education purposes only.

Acknowledgements

This data is solely belongs to https://www.worldometers.info/coronavirus/. for licensing visit https://www.worldometers.info/licensing/

This dataset results from DG REGIO calculations based on Eurostat data (demo_r_mwk3_t). It presents excess mortality comparisons of the number of deaths that occurred in 2020 and 2021 with the average number of deaths that occurred in the corresponding weeks of 2015 to 2019. The age structure of the population and the deaths is not taken into account. The figures shown are rolling three week averages centred around the week in question. Access the EUROSTAT data on their webpage - deaths by week and NUTS region - https://ec.europa.eu/eurostat/databrowser/view/demo_r_mwk3_t/default/table?lang=en - and see the EUROSTAT webpage on national and regional weekly death statistics - https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Weekly_death_statistics Data is not available for Ireland. For Italy no data is available for the last weeks of 2021. This dataset presents a vertical / narrow view of the longitudinal timeseries data for 2020-2021. This dataset - https://cohesiondata.ec.europa.eu/Other/2020-2021-NUTS-Excess-mortality-3-week-average-hor/kzsy-bycf - provides the same values in a horizontal / wide format.

Italy IT: Number of Deaths Ages 5-9 Years data was reported at 205.000 Person in 2019. This records a decrease from the previous number of 207.000 Person for 2018. Italy IT: Number of Deaths Ages 5-9 Years data is updated yearly, averaging 272.500 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 523.000 Person in 1992 and a record low of 204.000 Person in 2016. Italy IT: Number of Deaths Ages 5-9 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Health Statistics. Number of deaths of children ages 5-9 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Flood mortality is still a serious concern in both developed and developing countries, requiring a deeper understanding to identify hazardous factors and mitigate the life losses. with this database, we compared the flood fatalities occurred in the period 1990-2022 in two Mediterranean regions characterized by different natural and anthropogenic frameworks and located in western Algeria and southern Italy, respectively. The main goal is to detect, either common features controlling flood mortality or typical factors causing local differences among the two areas, in order to identify the drivers of flood mortality and suggest how alleviate their impact applying mitigation strategies customized to the detected failures. With these purposes we created the database containing information 242 flood fatalities occurred in the two regions in the 33-year study period, including time and place of fatal accidents, age and gender of the victims, death circumstances and victim’s behavior.

Italy IT: Probability of Dying at Age 15-19 Years: per 1000 data was reported at 1.100 Ratio in 2019. This stayed constant from the previous number of 1.100 Ratio for 2018. Italy IT: Probability of Dying at Age 15-19 Years: per 1000 data is updated yearly, averaging 1.850 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 2.800 Ratio in 1991 and a record low of 1.100 Ratio in 2019. Italy IT: Probability of Dying at Age 15-19 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Health Statistics. Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Analysis of ‘Missing Migrants Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jmataya/missingmigrants on 14 February 2022.

--- Dataset description provided by original source is as follows ---

About the Missing Migrants Data

This data is sourced from the International Organization for Migration. The data is part of a specific project called the Missing Migrants Project which tracks deaths of migrants, including refugees , who have gone missing along mixed migration routes worldwide. The research behind this project began with the October 2013 tragedies, when at least 368 individuals died in two shipwrecks near the Italian island of Lampedusa. Since then, Missing Migrants Project has developed into an important hub and advocacy source of information that media, researchers, and the general public access for the latest information.

Where is the data from?

Missing Migrants Project data are compiled from a variety of sources. Sources vary depending on the region and broadly include data from national authorities, such as Coast Guards and Medical Examiners; media reports; NGOs; and interviews with survivors of shipwrecks. In the Mediterranean region, data are relayed from relevant national authorities to IOM field missions, who then share it with the Missing Migrants Project team. Data are also obtained by IOM and other organizations that receive survivors at landing points in Italy and Greece. In other cases, media reports are used. IOM and UNHCR also regularly coordinate on such data to ensure consistency. Data on the U.S./Mexico border are compiled based on data from U.S. county medical examiners and sheriff’s offices, as well as media reports for deaths occurring on the Mexico side of the border. Estimates within Mexico and Central America are based primarily on media and year-end government reports. Data on the Bay of Bengal are drawn from reports by UNHCR and NGOs. In the Horn of Africa, data are obtained from media and NGOs. Data for other regions is drawn from a combination of sources, including media and grassroots organizations. In all regions, Missing Migrants Projectdata represents minimum estimates and are potentially lower than in actuality.

Updated data and visuals can be found here: https://missingmigrants.iom.int/

Who is included in Missing Migrants Project data?

IOM defines a migrant as any person who is moving or has moved across an international border or within a State away from his/her habitual place of residence, regardless of

  (1) the person’s legal status; 
  (2) whether the movement is voluntary or involuntary; 
  (3) what the causes for the movement are; or 
  (4) what the length of the stay is.[1]

Missing Migrants Project counts migrants who have died or gone missing at the external borders of states, or in the process of migration towards an international destination. The count excludes deaths that occur in immigration detention facilities, during deportation, or after forced return to a migrant’s homeland, as well as deaths more loosely connected with migrants’ irregular status, such as those resulting from labour exploitation. Migrants who die or go missing after they are established in a new home are also not included in the data, so deaths in refugee camps or housing are excluded. This approach is chosen because deaths that occur at physical borders and while en route represent a more clearly definable category, and inform what migration routes are most dangerous. Data and knowledge of the risks and vulnerabilities faced by migrants in destination countries, including death, should not be neglected, rather tracked as a distinct category.

How complete is the data on dead and missing migrants?

Data on fatalities during the migration process are challenging to collect for a number of reasons, most stemming from the irregular nature of migratory journeys on which deaths tend to occur. For one, deaths often occur in remote areas on routes chosen with the explicit aim of evading detection. Countless bodies are never found, and rarely do these deaths come to the attention of authorities or the media. Furthermore, when deaths occur at sea, frequently not all bodies are recovered - sometimes with hundreds missing from one shipwreck - and the precise number of missing is often unknown. In 2015, over 50 per cent of deaths recorded by the Missing Migrants Project refer to migrants who are presumed dead and whose bodies have not been found, mainly at sea.

Data are also challenging to collect as reporting on deaths is poor, and the data that does exist are highly scattered. Few official sources are collecting data systematically. Many counts of death rely on media as a source. Coverage can be spotty and incomplete. In addition, the involvement of criminal actors in incidents means there may be fear among survivors to report deaths and some deaths may be actively covered-up. The irregular immigration status of many migrants, and at times their families as well, also impedes reporting of missing persons or deaths.

The varying quality and comprehensiveness of data by region in attempting to estimate deaths globally may exaggerate the share of deaths that occur in some regions, while under-representing the share occurring in others.

What can be understood through this data?

The available data can give an indication of changing conditions and trends related to migration routes and the people travelling on them, which can be relevant for policy making and protection plans. Data can be useful to determine the relative risks of irregular migration routes. For example, Missing Migrants Project data show that despite the increase in migrant flows through the eastern Mediterranean in 2015, the central Mediterranean remained the more deadly route. In 2015, nearly two people died out of every 100 travellers (1.85%) crossing the Central route, as opposed to one out of every 1,000 that crossed from Turkey to Greece (0.095%). From the data, we can also get a sense of whether groups like women and children face additional vulnerabilities on migration routes.

However, it is important to note that because of the challenges in data collection for the missing and dead, basic demographic information on the deceased is rarely known. Often migrants in mixed migration flows do not carry appropriate identification. When bodies are found it may not be possible to identify them or to determine basic demographic information. In the data compiled by Missing Migrants Project, sex of the deceased is unknown in over 80% of cases. Region of origin has been determined for the majority of the deceased. Even this information is at times extrapolated based on available information – for instance if all survivors of a shipwreck are of one origin it was assumed those missing also came from the same region.

The Missing Migrants Project dataset includes coordinates for where incidents of death took place, which indicates where the risks to migrants may be highest. However, it should be noted that all coordinates are estimates.

Why collect data on missing and dead migrants?

By counting lives lost during migration, even if the result is only an informed estimate, we at least acknowledge the fact of these deaths. What before was vague and ill-defined is now a quantified tragedy that must be addressed. Politically, the availability of official data is important. The lack of political commitment at national and international levels to record and account for migrant deaths reflects and contributes to a lack of concern more broadly for the safety and well-being of migrants, including asylum-seekers. Further, it drives public apathy, ignorance, and the dehumanization of these groups.

Data are crucial to better understand the profiles of those who are most at risk and to tailor policies to better assist migrants and prevent loss of life. Ultimately, improved data should contribute to efforts to better understand the causes, both direct and indirect, of fatalities and their potential links to broader migration control policies and practices.

Counting and recording the dead can also be an initial step to encourage improved systems of identification of those who die. Identifying the dead is a moral imperative that respects and acknowledges those who have died. This process can also provide a some sense of closure for families who may otherwise be left without ever knowing the fate of missing loved ones.

Identification and tracing of the dead and missing

As mentioned above, the challenge remains to count the numbers of dead and also identify those counted. Globally, the majority of those who die during migration remain unidentified. Even in cases in which a body is found identification rates are low. Families may search for years or a lifetime to find conclusive news of their loved one. In the meantime, they may face psychological, practical, financial, and legal problems.

Ultimately Missing Migrants Project would like to see that every unidentified body, for which it is possible to recover, is adequately “managed”, analysed and tracked to ensure proper documentation, traceability and dignity. Common forensic protocols and standards should be agreed upon, and used within and between States. Furthermore, data relating to the dead and missing should be held in searchable and open databases at local, national and international levels to facilitate identification.

For more in-depth analysis and discussion of the numbers of missing and dead migrants around the world, and the challenges involved in identification and tracing, read our two reports on the issue, Fatal Journeys: Tracking Lives Lost during Migration (2014) and Fatal Journeys Volume 2, Identification and Tracing of Dead and Missing Migrants

Content

The data set records

This Project Tycho dataset includes a CSV file with COVID-19 data reported in ITALY: 2019-12-30 - 2021-07-31. It contains counts of cases, deaths, and hospitalizations. Data for this Project Tycho dataset comes from: "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University", "Presidenzia del Consiglio dei Ministri Dipartimento della Protezione Civile GitHub Repository", "European Centre for Disease Prevention and Control Website", "World Health Organization COVID-19 Dashboard". The data have been pre-processed into the standard Project Tycho data format v1.1.

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.

Italy IT: Probability of Dying at Age 20-24 Years: per 1000 data was reported at 1.600 Ratio in 2019. This stayed constant from the previous number of 1.600 Ratio for 2018. Italy IT: Probability of Dying at Age 20-24 Years: per 1000 data is updated yearly, averaging 2.550 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 3.700 Ratio in 1991 and a record low of 1.500 Ratio in 2017. Italy IT: Probability of Dying at Age 20-24 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Health Statistics. Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

AimTo carry out a nationwide evaluation of both in- and out-of-hospital mortality for acute myocardial infarction (AMI) during the COVID-19 pandemic period in Italy.MethodsThis was a retrospective cohort study analysing overall mortality for AMI in Italy during the COVID-19 pandemic (March 1st, 2020–December 31st, 2021) and the previous 5 years (January 1st, 2015–February 29th, 2020). To carefully analyze both in- and out-of-hospital mortality for AMI (with or without concomitant COVID-19 infection) we used different institutional administrative sources of national data. Excess mortality related to AMI during the COVID-19 pandemic has been analyzed using the observed/expected ratio (OER).ResultsOver the 5 years pre-pandemic period, 150,299 fatal events related to AMI occurred. During the pandemic, the number of deaths related to AMI was 28,673 in 2020 and declined to 26,688 in 2021. The overall OER was 1.18 [95% confidence intervals (CI): 1.15–1.22] in 2020 and 1.19 (95% CI: 1.15–1.22) while out-of-hospital OER was 1.24 (95% CI: 1.20–1.29) in 2020 and 1.21 (95% CI: 1.16–1.25) during the pandemic. When excluding COVID-19 related deaths, the number of observed in-hospital deaths did not significantly differ from the expected both in 2020 and 2021 while the excess remains unchanged for out-of-hospital mortality.ConclusionsIn this analysis of nationwide institutional administrative databases, we documented an increase in observed mortality compared to the expected during the COVID-19 pandemic in Italy. This mortality increase is mainly attributable to out-of-hospital fatal events and related to concomitant COVID-19 infection for hospitalized AMI patients.

Context

This dataset contains a time series of Covid-19 data (confirmed cases, recoveries, deaths) worldwide (24,628 countries) and for 4 selected regions: Brazil, Canada, China, Italy. The time series is taken from Jan-22-2020 through Apr-24-2020.

Content

This dataset consists of the following files:

brazil_province_wise.csv canada_province_wise.csv china_province_wise.csv italy_province_wise.csv

Acknowledgements

This dataset was collected from the following repository: https://github.com/imdevskp/covid_19_jhu_data_web_scrap_and_cleaning Pre-processing was achieved with the directions provided in the repository.

Inspiration

With this dataset, we'd like to explore the evolution of COVID-19 contamination worldwide and answer some of the most pondered questions: what are the best approaches for battling infections? What are the predictions for the re-opening of common activities?

Ref: https://github.com/CSSEGISandData/COVID-19 Daily reports (csse_covid_19_daily_reports) This folder contains daily case reports. All timestamps are in UTC (GMT+0). File naming convention MM-DD-YYYY.csv in UTC. Field description Province/State: China - province name; US/Canada/Australia/ - city name, state/province name; Others - name of the event (e.g., "Diamond Princess" cruise ship); other countries - blank. Country/Region: country/region name conforming to WHO (will be updated). Last Update: MM/DD/YYYY HH:mm (24 hour format, in UTC). Confirmed: the number of confirmed cases. For Hubei Province: from Feb 13 (GMT +8), we report both clinically diagnosed and lab-confirmed cases. For lab-confirmed cases only (Before Feb 17), please refer to who_covid_19_situation_reports. For Italy, diagnosis standard might be changed since Feb 27 to "slow the growth of new case numbers." (Source) Deaths: the number of deaths. Recovered: the number of recovered cases. Update frequency Files after Feb 1 (UTC): once a day around 23:59 (UTC). Files on and before Feb 1 (UTC): the last updated files before 23:59 (UTC). Sources: archived_data and dashboard. Data sources Refer to the mainpage. Why create this new folder? Unifying all timestamps to UTC, including the file name and the "Last Update" field. Pushing only one file every day. All historic data is archived in archived_data. Time series summary (csse_covid_19_time_series) This folder contains daily time series summary tables, including confirmed, deaths and recovered. All data are from the daily case report. Field descriptioin Province/State: same as above. Country/Region: same as above. Lat and Long: a coordinates reference for the user. Date fields: M/DD/YYYY (UTC), the same data as MM-DD-YYYY.csv file.