The first two cases of the new coronavirus (COVID-19) in Italy were recorded between the end of January and the beginning of February 2020. Since then, the number of cases in Italy increased steadily, reaching over 26.9 million as of January 8, 2025. The region mostly hit by the virus in the country was Lombardy, counting almost 4.4 million cases. On January 11, 2022, 220,532 new cases were registered, which represented the biggest daily increase in cases in Italy since the start of the pandemic. 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.

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.

Since the spread of the coronavirus (COVID-19) in Italy, started in February 2020, many people who contracted the infection died. The number of deaths amounted to 198,683 as of January 8, 2025. On December 3, 2020, 993 patients died, the highest daily toll since the start of the pandemic. The region with the highest number of deaths was Lombardy, which is also the region that registered the highest number of coronavirus 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, roughly 85 percent of the total Italian population was fully vaccinated. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

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.

This paper presents a novel dataset of non-pharmaceutical interventions adopted by Italian authorities to tackle the COVID-19 pandemic at the national and local levels. The dataset follows the structure of the Oxford Coronavirus Government Response Tracker (OxCGRT; Hale et al. in Nat Human Behav 5:529–538, https://doi.org/10.1038/s41562-021-01079-8, 2021)). We include several novelties with respect to the original source. First, we tailor the classification of provisions to the measures adopted in Italy. Second, we collect detailed information on local restrictions in the country, including lockdowns and school closures. Third, we apply a bottom-up approach to construct population-weighted average stringency indexes (Italian Stringency Indexes, ItSIs) at the provincial, regional, and country-wide levels. While expanding the geographical coverage of the stringency indicators, we preserve the comparability of the ItSIs with the original stringency index published in the OxCGRT. As an application, we show that the correlations of our ItSI with community mobility indicators and various measures of economic activity are higher than those obtained with the OxCGRT indicator.

What impact has the COVID-19 pandemic had on Italians' attitudes, opinions, and behaviors? From this question, the ResPOnsE COVID-19 project (Response of Italian Public Opinion to the COVID-19 Emergency) was developed starting in March 2020, with the aim of building a research infrastructure for the daily monitoring of public opinion during the COVID-19 emergency. The collection of daily information through online interviews (CAWI) to a sample reflecting the distribution of the Italian population by gender and area of residence was divided into four surveys that took place between April 2020 and December 2021, for a total of more than 30,000 interviews. The infrastructure was designed by the spsTREND "Hans Schadee" laboratory in collaboration with the SWG institute, as part of the "Departments of Excellence 2018-2022" project promoted by the Ministry of University and Research and is supported by funding from the Cariplo Foundation. Overall Research Design The research design included four surveys (waves) following a repeated cross-sectional design, consistent with the dynamic nature of the pandemic phenomenon. The four waves of ResPOnsE COVID-19 are distributed as follows. First wave: from April 6 to July 6, 2020 (~15000 cases, RR=46,6%) Second wave: from December 21, 2020 to January 2, 2021 (~3000 cases, RR=47%) Third wave: from March 17 to June 16, 2021 (~9300 cases, RR=76.9%) Fourth wave: from November 10 to December 22, 2021 (~3000 cases, RR=67.1%) Rolling Cross-Section and Panel Design The first, third, and fourth waves collect interviews through a Rolling Cross-Section (RCS) design, that is consecutive daily samples for a relatively long period (in this case 2 to 3 months). In addition, about 60% of subjects were interviewed twice between the first and third or fourth wave, thus allowing longitudinal analysis of intra-individual variations that occurred between 2020 and 2021. An RCS survey can be viewed as a cross-sectional survey of a single sample that is, however, "sliced" into many equivalent small subgroups that are released on consecutive days. On the day of release, individuals belonging to a particular sub-group are invited to participate in the survey. The distinguishing feature of the RCS design, however, is that these individuals can also respond in the days following the delivery of the invitation. Hence comes the term "rolling" meaning that the overall sample "rolls" through the days of the survey, making time (days) a random variable. The daily samples are mutually independent and the estimates derived for each are comparable. In this way, the RCS design is optimal for studying trends in the case of time-varying phenomena. For details, see the articles by Vezzoni et al. (2020) and Biolcati et al. (2021). Questionnaire structure The questionnaire administered in the ResPOnsE COVID-19 survey consists of a main questionnaire, containing a core set of questions repeated in each of the four surveys, and one or more thematic modules that may change with each survey. The main questionnaire consists of eleven thematic sections covering the entire survey period. Most of the questions in the questionnaire were repeated in the four surveys, while some questions were eliminated/changed or new ones were introduced in the transition to a new survey. Covering the entire survey period, the basic module is particularly suitable for diachronic analysis, while the structure of the thematic modules, usually collected over a few weeks, suggests an analysis of them with a cross-sectional approach. Source questionnaires in Italian are available for download. The sample The target population consists of Italian residents aged 18 years and older. In the RCS waves, on average, between 100 and 150 interviews were conducted each day, corresponding to about 1,000 interviews per week for the first survey and about 700 for the third and fourth surveys (the interviews in the second survey were actually concentrated in a single week), for a total of 31,122 interviews. Given time and resource constraints, probabilistic sampling could not be used. Instead, the samples are drawn from an online community of a commercial research institute (SWG SpA). To correct against expected bias, the sample is stratified by ISTAT macro-area of residence and composed of quotas defined by gender and age. Weights have also been created for carryover to the population. Detailed instructions on using the weights can be downloaded together with the data files. The survey also includes a panel component: about 60 percent of subjects were interviewed twice between the first, third, and fourth waves. Over-sampling was also conducted for the Lombardy region, for which 1124 additional cases are available in the third wave Macro level data The cumulative data file also includes official macro-level variables capturing daily information on the health emergency, such as the number of people infected by...

The COVID-19 pandemic exerted an extraordinary pressure on the Italian healthcare system (Sistema Sanitario Nazionale, SSN), determining an unprecedented health crisis. In this context, a multidisciplinary non-governmental initiative called Italian Response to COVID-19 (IRC-19) was implemented from June 2020 to August 2021 to support the Italian health system through multiple activities aimed to mitigate the effects of the pandemic. The objective of this study was to shed light on the role of NGOs in supporting the SSN during the first pandemic wave by specifically exploring: (1) the main challenges experienced by Italian hospitals and out-of-hospital care facilities and (2) the nature and extent of the IRC-19 interventions specifically implemented to support healthcare facilities, to find out if and how such interventions met healthcare facilities' perceived needs at the beginning of the pandemic. We conducted a cross-sectional study using an interviewer administered 32-item questionnaire among 14 Italian healthcare facilities involved in the IRC-19 initiative. Health facilities' main challenges concerned three main areas: healthcare workers, patients, and facilities' structural changes. The IRC-19 initiative contributed to support both hospital and out-of-hospital healthcare facilities by implementing interventions for staff and patients' safety and flow management and interventions focused on the humanization of care. The support from the third sector emerged as an added value that strengthened the Italian response to the COVID-19 pandemic. This is in line with the Health—Emergency and Disaster Risk Management (H-EDRM) precepts, that call for a multisectoral and multidisciplinary collaboration for an effective disaster management.

Introduction: In 2021, the European Medicines Agency supported the “Covid Vaccine Monitor (CVM),” an active surveillance project spanning 13 European countries aimed at monitoring the safety of COVID-19 vaccines in general and special populations (i.e., pregnant/breastfeeding women, children/adolescents, immunocompromised people, and people with a history of allergies or previous SARS-CoV-2 infection). Italy participated in this project as a large multidisciplinary network called the “ilmiovaccinoCOVID19 collaborating group.”Methods: The study aimed to describe the experience of the Italian network “ilmiovaccinoCOVID19 collaborating group” in the CVM context from June 2021 to February 2023. Comprising about 30 partners, the network aimed to facilitate vaccinee recruitment. Participants completed baseline and follow-up questionnaires within 48 h from vaccination over a 6-month period. Analyses focused on those who completed both the baseline and the first follow-up questionnaire (Q1), exploring temporal trends, vaccination campaign correlation, and loss to follow-up. Characteristics of recruited vaccinees and vaccinee-reported adverse drug reactions (ADRs) were compared with passive surveillance data in Italy.Results: From June 2021 to November 2022, 22,384,663 first doses and 38,207,452 booster doses of COVID-19 vaccines were administered in Italy. Simultaneously, the study enrolled 1,229 and 2,707 participants for the first and booster doses, respectively. Of these, 829 and 1,879 vaccinees, respectively, completed both baseline and at least Q1 and were included in the analyses, with a significant proportion of them (57.8%/34.3%) belonging to special cohorts. Most vaccinees included in the analyses were women. Comirnaty® (69%) and Spikevax® (29%) were the most frequently administered vaccines. ADR rates following Comirnaty® and Spikevax® were higher after the second dose, particularly following Spikevax®. Serious ADRs were infrequent. Differences were observed in ADR characteristics between CVM and Italian passive surveillance.Conclusion: This study confirmed the favorable safety profile of COVID-19 vaccines, with findings consistent with pivotal clinical trials of COVID-19 vaccines, although different proportions of serious ADRs compared to spontaneous reporting were observed. Continuous evaluation through cohort event monitoring studies provides real-time insights crucial for regulatory responses. Strengthening infrastructure and implementing early monitoring strategies are essential to enhance vaccine safety assessment and prepare for future pandemics.

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.

The Delta variant became dominant during the second wave of the Covid-19 pandemic due to its competitive advantage, the ability to reduce close contact duration from minutes to seconds, and, consequently, increase the risk of exposure to COVID-19. We used game theory to model the most effective public health response to this new threat. We compared the absolute and relative risk of exposure to COVID-19 before and after the emergence of the Delta variant. The absolute risk of exposure was defined as the product of crowding (people within a six feet distance) and visit duration. Our epidemiological investigation used aggregated and anonymized mobility data from Google Maps to estimate the visit duration for 808 premises in the metropolitan area of Genoa, Italy, in June 2021. The relative risk of exposure was obtained by dividing the risk of exposure of each activity by the lowest value (gas stations = 1). The median absolute risk of exposure to COVID-19 increased by sixty-fold in the first semester of 2021, while the relative risk did not significantly differ from the risk of exposure to the ancestral form of Covid-19 (5.9 in 2021 vs. 2.5 in 2021). The Delta variant represents an evolution of the game against COVID-19, but it is not a game-changer. The best response is to commit to our original strategy based on population-wide vaccination and social distancing. Unilateral deviations from the dominant strategy could offer COVID-19 a fighting chance against humanity.

Dataset from Magon A, Arrigoni C, Barello S, Graffigna G, Caruso R. Managing anticoagulation in the COVID-19 era between lockdown and reopening phases: Comment. Intern Emerg Med. 2021 Oct;16(7):2017-2018. doi: 10.1007/s11739-021-02647-6. Epub 2021 Feb 10. PMID: 33566279; PMCID: PMC7873665.

Dataset from the article Biagioli V, Albanesi B, Belloni S, Piredda A, Caruso R. Living with cancer in the COVID-19 pandemic: An Italian survey on self-isolation at home. Eur J Cancer Care (Engl). 2021 Mar;30(2):e13385. doi: 10.1111/ecc.13385. Epub 2020 Dec 7. PMID: 33289205; PMCID: PMC7883078.

Abstract

Objective: To investigate the perception of self-isolation at home in patients with cancer during the lockdown period resulting from the COVID-19 outbreak in Italy.

Methods: A cross-sectional descriptive study was conducted through an online survey of patients with cancer who were sheltering at home from 29th March to 3rd May 2020. Perception of self-isolation was assessed using the ISOLA scale, after evaluation of its psychometric properties. Content analysis was used to analyse two open-ended questions.

Results: The participants were 195 adult patients with cancer (female = 76%; mean age = 50.3 ± 11.2; haematological malignancy = 51.3%). They reported moderate isolation-related suffering (M = 2.64 ± 0.81), problems in their relationships with others (M = 3.31 ± 1.13) and difficulties in their relationships with themselves (M = 3.14 ± 1.06). Patients who experienced significantly more social problems were older, had less education and were living without minor children. Overall, four main categories emerged from the qualitative content analysis: (1) lack of freedom and social life, (2) uncertainty and worries, (3) feeling supported and (4) dealing with isolation.

Conclusion: Living with cancer in the COVID-19 pandemic was often perceived as an isolating experience, primarily in terms of detachment from loved ones.

Dataset from Barello S, Caruso R, Palamenghi L, Nania T, Dellafiore F, Bonetti L, Silenzi A, Marotta C, Graffigna G. Factors associated with emotional exhaustion in healthcare professionals involved in the COVID-19 pandemic: an application of the job demands-resources model. Int Arch Occup Environ Health. 2021 Nov;94(8):1751-1761. doi: 10.1007/s00420-021-01669-z. Epub 2021 Mar 3. PMID: 33660030; PMCID: PMC7928172.

Abstract

Purpose: The purpose of the present cross-sectional study is to investigate the role of perceived COVID-19-related organizational demands and threats in predicting emotional exhaustion, and the role of organizational support in reducing the negative influence of perceived COVID-19 work-related stressors on burnout. Moreover, the present study aims to add to the understanding of the role of personal resources in the Job Demands-Resources model (JD-R) by examining whether personal resources-such as the professionals' orientation towards patient engagement-may also strengthen the impact of job resources and mitigate the impact of job demands.

Methods: This cross-sectional study involved 532 healthcare professionals working during the COVID-19 pandemic in Italy. It adopted the Job-Demands-Resource Model to study the determinants of professional's burnout. An integrative model describing how increasing job demands experienced by this specific population are related to burnout and in particular to emotional exhaustion symptoms was developed.

Results: The results of the logistic regression models provided strong support for the proposed model, as both Job Demands and Resources are significant predictors (OR = 2.359 and 0.563 respectively, with p < 0.001). Moreover, healthcare professionals' orientation towards patient engagement appears as a significant moderator of this relationship, as it reduces Demands' effect (OR = 1.188) and increases Resources' effect (OR = 0.501).

Conclusions: These findings integrate previous findings on the JD-R Model and suggest the relevance of personal resources and of relational factors in affecting professionals' experience of burnout.

BackgroundVaccines for coronavirus disease 2019 (COVID-19) are proving to be very effective in preventing severe illness; however, although rare, post-vaccine infections have been reported. The present study focuses on virological and serological features of 94 infections that occurred in Lazio Region (Central Italy) between 27 December 2020, and 30 March 2021, after one or two doses of mRNA BNT162b2 vaccine.MethodsWe evaluated clinical features, virological (viral load; viral infectiousness; genomic characterisation), and serological (anti-nucleoprotein Ig; anti-Spike RBD IgG; neutralising antibodies, nAb) characteristics of 94 post-vaccine infections at the time of diagnosis. Nasopharyngeal swabs (NPSs) and serum samples were collected in the framework of the surveillance activities on SARS-CoV-2 variants established in Lazio Region (Central Italy) and analysed at the National Institute for Infectious Diseases “L. Spallanzani” in Rome.ResultsThe majority (92.6%) of the post-vaccine infections showed pauci/asymptomatic or mild clinical course, with symptoms and hospitalisation rate significantly less frequent in patients infected after full vaccination course as compared to patients who received a single dose vaccine. Although differences were not statistically significant, viral loads and isolation rates were lower in NPSs from patients infected after receiving two vaccine doses as compared to patients with one dose. Most cases (84%) had nAb in serum at the time of infection diagnosis, which is a sub-group of vaccinees, were found similarly able to neutralise Alpha and Gamma variants. Asymptomatic individuals showed higher nAb titres as compared to symptomatic cases (median titre: 1:120 vs. 1:40, respectively). Finally, the proportion of post-vaccine infections attributed either to Alpha and Gamma variants was similar to the proportion observed in the contemporary unvaccinated population in the Lazio region, and mutational analysis did not reveal enrichment of a defined set of Spike protein substitutions depending on the vaccination status.ConclusionOur study conducted using real-life data, emphasised the importance of monitoring vaccine breakthrough infections, through the characterisation of virological, immunological, and clinical features associated with these events, in order to tune prevention measures in the next phase of the COVID-19 pandemic.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

As of November 24, 2024 there were over 274 million confirmed cases of coronavirus (COVID-19) across the whole of Europe since the first confirmed cases in France in January 2020. France has been the worst affected country in Europe with 39,028,437 confirmed cases, followed by Germany with 38,437,756 cases. Italy and the UK have approximately 26.8 million and 25 million cases respectively. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

During autumn 2021, Italian authorities started administering booster shots of coronavirus vaccines to the population in order to improve the general protection against the virus, using either Pfizer or Moderna vaccines. As of September 24, 2023, approximately 70.3 percent of the population over 12 years old have received a booster vaccination. This statistic shows regional differences in these figures. Lombardy was the region achieving the highest booster vaccination coverage, with around 74.3 percent. Conversely, just 61.5 percent of citizens living in Sicily received a third shot. About 85 percent of the total population in Italy has completed the regular vaccination cycle, having received two shots. Thanks to this, despite the high number of daily cases, figures for deaths and hospitalizations remain low. More statistics and facts about the virus in Italy are available here.For a global overview on the various COVID-19 vaccines' development and distribution, visit Statista's Facts and Figures on the topic.

As of January 13, 2023, Bulgaria had the highest rate of COVID-19 deaths among its population in Europe at 548.6 deaths per 100,000 population. Hungary had recorded 496.4 deaths from COVID-19 per 100,000. Furthermore, Russia had the highest number of confirmed COVID-19 deaths in Europe, at over 394 thousand.

Number of cases in Europe During the same period, across the whole of Europe, there have been over 270 million confirmed cases of COVID-19. France has been Europe's worst affected country with around 38.3 million cases, this translates to an incidence rate of approximately 58,945 cases per 100,000 population. Germany and Italy had approximately 37.6 million and 25.3 million cases respectively.

Current situation In March 2023, the rate of cases in Austria over the last seven days was 224 per 100,000 which was the highest in Europe. Luxembourg and Slovenia both followed with seven day rates of infections at 122 and 108 respectively.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.