As of March 20, 2023, around 391 doses of COVID-19 vaccines per 100 people in Cuba had been administered, one of the highest COVID-19 vaccine dose rates of any country worldwide. This statistic shows the rate of COVID-19 vaccine doses administered worldwide as of March 20, 2023, by country or territory.

As of January 18, 2023, Portugal had the highest COVID-19 vaccination rate in Europe having administered 272.78 doses per 100 people in the country, while Malta had administered 258.49 doses per 100. The UK was the first country in Europe to approve the Pfizer/BioNTech vaccine for widespread use and began inoculations on December 8, 2020, and so far have administered 224.04 doses per 100. At the latest data, Belgium had carried out 253.89 doses of vaccines per 100 population. Russia became the first country in the world to authorize a vaccine - named Sputnik V - for use in the fight against COVID-19 in August 2020. As of August 4, 2022, Russia had administered 127.3 doses per 100 people in the country.

The seven-day rate of cases across Europe shows an ongoing perspective of which countries are worst affected by the virus relative to their population. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

As of April 25, 2022, Mauritius was the African country with the highest number of coronavirus (COVID-19) doses secured per capita. The country had received **** COVID-19 vaccine doses per capita through bilateral agreements, donations, and the COVAX initiative. Seychelles and Rwanda followed with **** and **** doses per capita, respectively.

Context

All about an attempt to end the pandemic across the globe with the help of vaccinations for COVID-19. It is important to track and understand the effort that is in progress across the globe to administer doses of vaccinations. There could be many sources of information. This is one of the sources from Bloomberg that is captured and presented here. Additionally, I have tried to include the GDP per capita per country from Wiki so that we can see how that is influencing the vaccination progress.

Content

There are two files. a) Latest Global Covid-19 Vaccine tracker of all the countries and regions in the World as of September 11, 2021 b) GDP information per capita per country

Attribute Information (COVID19 vaccination Tracker file)

• Countries and regions - Name of countries
• Doses administered - Number of vaccine doses administered
• Enough for % of people - Number of vaccine doses administered as a % of population
• Percentage of population with 1+ dose - Percentage of the population vaccinated with at least 1+ dose
• Percentage of the population fully vaccinated - Percentage of the population fully vaccinated
• Daily rate of doses administered - Daily rate of doses administered

Attribute Information (for GDP file per country per capita)

• Country
• Subregion (Western Europe, Northern Europe etc.)
• Region (Europe, Asia etc.)
• GDP estimate $ as per IMF
• Year for IMF
• GDP estimate $ as per UN
• Year for UN
• GDP estimate $ as per World Bank
• Year for World Bank

Source

URL1: https://www.bloomberg.com/graphics/covid-vaccine-tracker-global-distribution/ URL2: https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita

Inspiration

The path to immunity and hope to get back to normalcy by tracking and analyzing the latest updates on vaccinations across the globe. As we gear up to end the pandemic, the vaccination tracker can help us answer the following questions.

• What are the Top N countries/regions where vaccinations are administered?
• What are the Top N countries/regions with fully vaccinated people?
• What are the Top N countries/regions with at least 1+ doses administered?
• What is the access to vaccines - by least wealthy and most wealthy countries? (based on GDP per capita per country data)
• What is the average daily rate of the dose administered? Which countries are Top N and Bottom N? Which countries are above and below the World average? and many more...

Thank you for reading.

Please give your feedback/upvote/comments if you find this useful and download.

As of January 17, 2023, 96.3 percent of adults in Ireland had been fully vaccinated against COVID-19. According to the manufacturers of the majority of COVID-19 vaccines currently in use in Europe, being fully vaccinated is when a person receives two doses of the vaccine. In Portugal, 94.2 percent of adults had received a full course of the COVID-19 vaccination, as well as 93.9 percent of those in Malta had been fully vaccinated. On the other hand, only 35.8 percent of adults in Bulgaria had been fully vaccinated.

Furthermore, the seven-day rate of cases across Europe shows which countries are currently worst affected by the situation. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

The average for 2022 based on 187 countries was 84 percent. The highest value was in Antigua and Barbuda: 99 percent and the lowest value was in North Korea: 0 percent. The indicator is available from 1980 to 2022. Below is a chart for all countries where data are available.

Since 2001, an aid consortium known as Gavi has accounted for over half of vaccination expenditure in the 75 eligible countries with an initial per capita GNI below $1,000. Regression discontinuity (RD) estimates show aid significantly displaced other immunization efforts and failed to increase vaccination rates for diseases covered by cheap, existing vaccines. For some newer and more expensive vaccines, i.e., Hib and rotavirus, we found large effects on vaccination and limited fungibility, though statistical significance is not robust. These RD estimates apply to middle-income countries near Gavi's eligibility threshold, and cannot rule out differential effects for the poorest countries.

By August 2024, Cuba had administered the largest number of vaccines against COVID-19 per 100 inhabitants in the Latin American region, followed by Chile and Peru. According to recent estimates, the Caribbean country applied around 410 doses per 100 population, accounting for one of the largest vaccination rates observed not only in the Latin American region, but worldwide. In comparison, Haiti registered the lowest vaccination rate within the region, with only 5.87 doses administered per 100 inhabitants. Booster shots started To reinforce the immune protection against the fast spread of the SARS-CoV-2, governments began to introduce booster shots in their immunization programs aiming at strengthening people’s immune response against new contagious COVID-19 variants. In Latin America, Cuba was leading on booster shots relative to its population among a selection of countries, with around 88 percent of the population receiving the extra dose. In comparison, these numbers are higher than those for the European Union and the United States. Pharmaceutical research continues As Omicron becomes more prominent worldwide, and recombinant variants emerge, research efforts to prevent and control the disease continue to progress. As of June 2022, there were around 2,700 clinical trials to treat COVID-19 and 1,752 COVID-19 vaccines trials in clinical development. Other studies were focused on mild, moderate and severe COVID-19, complication support, and post-COVID symptoms, among others.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

IntroductionInequitable access to COVID-19 vaccines among countries is a pressing global health issue. Factors such as economic power, political power, political stability, and health system strength contribute to disparities in vaccine distribution. This study aims to assess the inequality in vaccine distribution among countries based on these factors and identify their relationship with COVID-19 vaccine distribution.MethodsA Concentration Index (CI) analysis was conducted to evaluate inequalities in the distribution of COVID-19 vaccines among countries based on four separate variables: GDP per capita, political stability (PS), World Power Index (WPI), and Universal Health Coverage (UHC). Additionally, Multiple Linear Regression (MLR) analysis was employed to explore the relationship between vaccine distribution and these independent variables. Two vaccine distribution variables were utilized for result reliability.ResultsThe analysis revealed significant inequalities in COVID-19 vaccine distribution according to the countries’ GDP/capita, PS, WPI, and UHC. However, the multiple linear regression analysis showed that there is no significant relationship between COVID-19 vaccine distribution and the countries’ GDP/capita and that UHC is the most influential factor impacting COVID-19 vaccine distribution and accessibility.DiscussionThe findings underscore the complex interplay between economic, political, and health system factors in shaping vaccine distribution patterns. To improve the accessibility to vaccines in future pandemics, Global Health Governance (GHG) and countries should consider working on three areas; enhance political stabilities in countries, separate the political power from decision-making at the global level and most importantly support countries to achieve UHC.

Context

Covid-19 Data collected from various sources on the internet. This dataset has daily level information on the number of affected cases, deaths, and recovery from the 2019 novel coronavirus. Please note that this is time-series data and so the number of cases on any given day is the cumulative number.

Content

The dataset includes 28 files scrapped from various data sources mainly the John Hopkins GitHub repository, the ministry of health affairs India, worldometer, and Our World in Data website. The details of the files are as follows

• countries-aggregated.csv A simple and cleaned data with 5 columns with self-explanatory names. -covid-19-daily-tests-vs-daily-new-confirmed-cases-per-million.csv A time-series data of daily test conducted v/s daily new confirmed case per million. Entity column represents Country name while code represents ISO code of the country. -covid-contact-tracing.csv Data depicting government policies adopted in case of contact tracing. 0 -> No tracing, 1-> limited tracing, 2-> Comprehensive tracing. -covid-stringency-index.csv The nine metrics used to calculate the Stringency Index are school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The index on any given day is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter response (i.e. 100 = strictest response). -covid-vaccination-doses-per-capita.csv A total number of vaccination doses administered per 100 people in the total population. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). -covid-vaccine-willingness-and-people-vaccinated-by-country.csv Survey who have not received a COVID vaccine and who are willing vs. unwilling vs. uncertain if they would get a vaccine this week if it was available to them. -covid_india.csv India specific data containing the total number of active cases, recovered and deaths statewide. -cumulative-deaths-and-cases-covid-19.csv A cumulative data containing death and daily confirmed cases in the world. -current-covid-patients-hospital.csv Time series data containing a count of covid patients hospitalized in a country -daily-tests-per-thousand-people-smoothed-7-day.csv Daily test conducted per 1000 people in a running week average. -face-covering-policies-covid.csv Countries are grouped into five categories: 1->No policy 2->Recommended 3->Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible 4->Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible 5->Required outside the home at all times regardless of location or presence of other people -full-list-cumulative-total-tests-per-thousand-map.csv Full list of total tests conducted per 1000 people. -income-support-covid.csv Income support captures if the government is covering the salaries or providing direct cash payments, universal basic income, or similar, of people who lose their jobs or cannot work. 0->No income support, 1->covers less than 50% of lost salary, 2-> covers more than 50% of the lost salary. -internal-movement-covid.csv Showing government policies in restricting internal movements. Ranges from 0 to 2 where 2 represents the strictest. -international-travel-covid.csv Showing government policies in restricting international movements. Ranges from 0 to 2 where 2 represents the strictest. -people-fully-vaccinated-covid.csv Contains the count of fully vaccinated people in different countries. -people-vaccinated-covid.csv Contains the total count of vaccinated people in different countries. -positive-rate-daily-smoothed.csv Contains the positivity rate of various countries in a week running average. -public-gathering-rules-covid.csv Restrictions are given based on the size of public gatherings as follows: 0->No restrictions 1 ->Restrictions on very large gatherings (the limit is above 1000 people) 2 -> gatherings between 100-1000 people 3 -> gatherings between 10-100 people 4 -> gatherings of less than 10 people -school-closures-covid.csv School closure during Covid. -share-people-fully-vaccinated-covid.csv Share of people that are fully vaccinated. -stay-at-home-covid.csv Countries are grouped into four categories: 0->No measures 1->Recommended not to leave the house 2->Required to not leave the house with exceptions for daily exercise, grocery shopping, and ‘essent...

Global Veterinary Vaccines Except for Foot and Mouth Market Size Value Per Capita by Country, 2023 Discover more data with ReportLinker!

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

ObjectivesTo compare the proportion of Beijing children who have ever had pneumonia (%Pneumonia) to those in other locations, and to estimate by how much national vaccine coverage with Pneumococcal Conjugate Vaccine (PCV) and Haemophilus Influenzae Type b (Hib) could reduce Beijing %Pneumonia.Methods%Pneumonia was obtained for each age group from 1 to 8 years inclusive from 5,876 responses to a cross-sectional questionnaire. Literature searches were conducted for world-wide reports of %Pneumonia. Previous vaccine trials conducted worldwide were used to estimate the pneumococcal (S. pneumoniae) and Hib (H. influenzae) burdens and %Pneumonia as well as the potential for PCV and Hib vaccines to reduce Beijing children’s %Pneumonia.FindingsThe majority of pneumonia cases occurred by the age of three. The cumulative %Pneumonia for 3–8 year-old Beijing children, 26.9%, was only slightly higher than the 25.4% for the discrete 3 year-old age group, similar to trends for Tianjin (China) and Texas (USA). Beijing’s %Pneumonia is disproportionally high relative to its Gross National Income (GNI) per capita, and markedly higher than %Pneumonia in the US and other high GNI per capita countries. Chinese diagnostic guidelines recommend chest X-ray confirmation while most other countries discourage it in favor of clinical diagnosis. Literature review shows that chest X-ray confirmation returns far fewer pneumonia diagnoses than clinical diagnosis. Accordingly, Beijing’s %Pneumonia is likely higher than indicated by raw numbers. Vaccine trials suggest that national PCV and Hib vaccination could reduce Beijing’s %Pneumonia from 26.9% to 19.7% and 24.9% respectively.ConclusionNational PCV and Hib vaccination programs would substantially reduce Beijing children’s pneumonia incidence.

Context

To be honest it's pretty hard for you to find data on vaccine progress and especially time-based data on a country like Pakistan. So, I created this small but interactive notebook that will keep updating the database until everyone is vaccinated. In this project I have used Pandas for easy WebSracping to get the data from pharmaceutical-technology.com then I have created Sqlite3 database to store the data into three tables. It took me a few tries to get everything working smooth so I started using SQL queries to get the data and then used plotly to plot interactive visualization. I was not sure when they will update the website so, I have created few functions to avoid duplication of data and to inform me on telegram about updates. I have also uploaded the processed data to Kaggle from Deepnote which will be updated daily. At last, I have used the Deepnote Schedule notebook feature to run this notebook every day and successfully publishing the article You can find my work on Deepnote.

Content

• World_Vaccination_Progress.csv -> Countries Vaccination progress
• pakistan_time_series.csv -> Time series data of Pakistan vaccine progress
• world_time_series.csv -> Time series data of World vaccine progress

Columns: - Country :: Names of countries in the world - Doses Administered: Total Doses Administered - Doses per 1000 : Number of Doses per thousand - Fully Vaccinated Population (%) : Percentage of a fully vaccinated person in a country. - Vaccine being used in a country : Types of vaccines used in a country.

For Time-Series

• Date_Time : Timestamp of entry

Acknowledgements

I am thankful for Pharmaceutical Technology for updating the stats on daily basis and publicly provide real-time stats of world's vaccination drive. I also want to thank Deepnote for the introduction of the Schedule notebook feature that has made this automation possible.

Inspiration

The lack of data available in my country drove me to create an automated system that collects data from web. You can read more about it in my article. The second inspiration came from participating in Deepnote competition which was on the data Vaccination drive of your country or World.

Typhoid fever remains a major public health threat in low- and middle-income countries (LMICs), where inadequate access to clean water and sanitation drives recurrent outbreaks. With antimicrobial resistance on the rise, the urgency of deploying preventive strategies such as typhoid conjugate vaccines (TCVs) have grown. In this study, we developed a dynamic compartmental model calibrated to the 2015 typhoid outbreak in Kampala, Uganda, to assess the health and economic outcomes of various outbreak response immunization (ORI) strategies using TCVs. We aimed to identify optimal ORI strategies that minimize cases and typhoid-related deaths as well as the costs of implementation. Our model incorporated different phases of the outbreak, vaccine coverage levels (30%, 50%, 70%), timing (early, late, combined), and campaign duration. Cost-effectiveness was evaluated based on disability-adjusted life years (DALYs) and incremental cost-effectiveness ratios (ICERs), using World Health Organization (WHO) thresholds derived from Uganda’s 2015 gross national income per capita. Early, high-coverage vaccination (Scenario 1) was most impactful reducing the effective reproduction number (Rt) below 1 during the epidemic peak and averting over 7,000 cases including 180 deaths. The timing of vaccine deployment was the most critical determinant of effectiveness, followed by coverage level and campaign duration. Our findings highlight the importance of rapid, high-coverage TCV deployment at the early stages of an outbreak. Strengthening disease surveillance and improving vaccine logistics are essential for a timely response. This modeling framework offers actionable evidence to support policy development and optimize outbreak preparedness in typhoid-endemic regions.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

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.