On January 13, 2023, Sweden registered 715 new coronavirus cases. The first case of coronavirus (COVID-19) in Sweden was confirmed on January 31, 2020. The number of cases in the country has since risen to a total of 2,687,840.

The worldwide number of confirmed cases of COVID-19 was over 668 million as of January 9, 2023. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

As of January 13, 2023, Sweden had reported 2,687,840 confirmed coronavirus cases. Cases first started to rise sharply in spring 2020, when the number of new confirmed cases per day started to increase, however the peak was much higher in winter 2021/22.

The novel coronavirus (COVID-19)

The coronavirus was officially declared as a worldwide pandemic by the World Health Organization on March 11, 2020. The novel coronavirus was first detected at a fish and seafood market in the Chinese city of Wuhan, in the Hubei province, in late December 2019. Since then, the virus reached over 668 million cases worldwide as of January 9, 2023.

Coronavirus-related deaths in Sweden

The first coronavirus related death in Sweden was reported on March 11, 2020 and as of January 13, 2023, the number of deaths reached a total of 22,645. The highest number of deaths occurred among the age group from 80 to 90 years old.

Context

Covid-19 is a global pandemic which requires a global effort to enable innovative solutions. We hope that this dataset will encourage such thinking and bring us closer to mapping an uncertain future for Sweden and the world.

Content

This data represents both confirmed cases and confirmed deaths from Covid-19 in Sweden by region per day. It is updated regularly and get transferred here as soon as an update is made. The data is collected from the National Health Agency of Sweden (Folkshälsomyndigheten) as well as regional health agencies for more up-to-date information.

Acknowledgements

All the credit for this dataset goes to Elin Lutz. All the data is updated from her Github repository https://github.com/elinlutz/gatsby-map.

Inspiration

The author also provides a live map of Sweden viewable at https://www.coronakartan.se/.

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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.

ECDC: COVID-2019: Number of Cases: Sweden data was reported at 0.000 Person in 14 Dec 2020. This stayed constant from the previous number of 0.000 Person for 13 Dec 2020. ECDC: COVID-2019: Number of Cases: Sweden data is updated daily, averaging 347.500 Person from Dec 2019 (Median) to 14 Dec 2020, with 350 observations. The data reached an all-time high of 8,402.000 Person in 10 Dec 2020 and a record low of 0.000 Person in 14 Dec 2020. ECDC: COVID-2019: Number of Cases: Sweden data remains active status in CEIC and is reported by European Centre for Disease Prevention and Control. The data is categorized under High Frequency Database’s Disease Outbreaks – Table ECDC.D001: Coronavirus Disease 2019 (COVID-2019): Cases and Deaths: by EU Member States (Discontinued).

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

WHO: Influenza A (H1N1): Confirmed Cases: Sweden data was reported at 84.000 Unit in 06 Jul 2009. This records an increase from the previous number of 74.000 Unit for 05 Jul 2009. WHO: Influenza A (H1N1): Confirmed Cases: Sweden data is updated daily, averaging 4.000 Unit from Apr 2009 (Median) to 06 Jul 2009, with 74 observations. The data reached an all-time high of 84.000 Unit in 06 Jul 2009 and a record low of 0.000 Unit in 05 May 2009. WHO: Influenza A (H1N1): Confirmed Cases: Sweden data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Heath Organization: Influenza A (H1N1): By Countries.

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.

After the noticeable growth of coronavirus cases in Sweden at the beginning of March 2020, the usage of gaming apps increased correspondingly, according to Airnow. To give an example, while roughly **** thousand Swedish people used the Minecraft app daily on their iOS devices on March 1, 2020, the number of daily active users (DAU) grew to over ** thousand as of April 20. Afterwards, the DAU of the sandbox video game declined again and was at about ** thousand on June 1.

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.

Collection of socio-economic and meteorological indicators as well as travel patterns and cases of H1N1 during the swine flu pandemic in Sweden in 2009. Comprise the supplementary information for the paper titled "Socioeconomic and environmental patterns behind H1N1 spreading in Sweden" by András Bóta, Martin Holmberg, Lauren Gardner and Martin Rosvall, Sci Rep 11, 22512 (2021). https://doi.org/10.1038/s41598-021-01857-4 Identifying the critical socio-economic, travel and climate factors related to influenza spreading is critical to the prediction and mitigation of epidemics. In the paper we study the 2009 A(H1N1) outbreak in the municipalities of Sweden, following it for six years between 2009 and 2015. Our goal is to discover the relationship between the above indicators and the timing of the epidemic onset of the disease. We also identify the municipalities playing a key role in the outbreak as well as the most critical travel routes of the country.

Publication available at: https://doi.org/10.1038/s41598-021-01857-4

Municipality codes for the municipalities of Sweden can be found here: https://www.scb.se/en/finding-statistics/regional-statistics/regional-divisions/counties-and-municipalities/counties-and-municipalities-in-numerical-order/

Data available according to Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license

Model inputs 1. giim_kommun_graph.csv Set of frequent travel routes between the municipalities of Sweden. The graph was constructed from "Trafikanalys, 2016. Resvanor. (accessed 26.8.19). Available from: http://www.trafa.se/RVU-Sverige/." using the methodology described in the paper. Date of construction: 2018-12-01 Format: csv Structure: edge list in (kommun1;kommun2) format with rows indicating a directed link between two municipalities. Municipalities are denoted according to their official municipal code

1. giim_casecounts.xlsx Number of new H1N1 cases in the municipalities of Sweden between 2009 and 2015. Our data set consists of all laboratory-verified cases of A(H1N1)pdm09 between May 2009 and December 2015, extracted from the SmiNet register of notifiable diseases, held by the Public Health Agency of Sweden. Due to confidentiality reasons, cases are anonymized, and addresses are aggregated at the DeSo level together with the date of diagnosis, age, and gender. We obtained ethical approval for the data acquisition. Date of construction: 2018-12-01 Format: xlsx Structure: Each tab represents a single flu season from the 2009/2010 season to the 2014/2015 season. Each tab is a matrix with rows indicating municipalities according to their official municipal code, and columns indicating epidemic weeks. Values of the matrices indicate the number of new laboratory-verified cases of A(H1N1)pdm09

2. giim_kommun_indicators.csv Socioeconomic and meteorological indicators are assigned to the municipalities of Sweden according to the methodology described in the paper. Indicators included are: a, mean temperature in degree Celsius, b, absolute humidity in grams per cubic metre, c, population size as the number of people living in each municipality, d, population density as the number of people per sq. km of land area, e, median income per household in thousand SEK, f, fraction of people on social aid (as a percentage), g, average number of children younger than 18 years per household. Meteorological data was obtained from the European Climate Assessment Dataset "Klein Tank A, Wijngaard J, Können G, Böhm R, Demarée G, Gocheva A, et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2002;22(12):1441–1453." Data from the dataset was converted to the municipality level according to the methodology described in the paper. Variables are mean temperature and relative humidity converted to absolute humidity for all municipalities of Sweden. Socioeconomic data was collected from Statistics Sweden between 2018 Ocotber and 2019 February. Available from: https://www.scb.se/en/. Variables are: The average household income as an economic indicator. The average number of children younger than 18 years per household to indicate family size. The fraction of people receiving social aid to represent poverty in a municipality. Population size and population density as the number of people per sq. km of land area. Date of construction: 2018-02-01 Format: csv Structure: Each row corresponds to a municipality denoted according to their official municipal code. Columns indicate socioeconomic and meteorological indicators as marked by the header row.

Model outputs 1. giim_export_risk.csv Exportation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate exportation risk values (should not be interpreted as probabilities).

1. giim_import_risk.csv Importation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate importation risk values (should not be interpreted as probabilities).

2. giim_transmission_prob.csv Transmission probabilities between all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Edge list with multiple edge weights. Rows indicate a directed link between the two municipalities (kommun1;kommun2) in the beginning of the row. The rest of the values in each row denote the corresponding transmission probabilities for each epidemic week computed according to the methodology described in the paper.

While the number of daily active users of the iOS Fortnite app in Sweden declined shortly after the coronavirus outbreak, the amount significantly grew from the end of March 2020. According to Airnow, the popular battle royal gaming app exceeded ******* daily active users in the Scandinavian country on May 1 and increased further. The first coronavirus case in Sweden was confirmed on February 4, 2020, but only since the beginning of March, the number considerably increased.

IntroductionThe traditional approach to epidemic control has been to slow down the rate of infection while building up healthcare capacity, resulting in a flattened epidemic curve. Advancements in bio-information-communication technology (BICT) have enabled the preemptive isolation of infected cases through efficient testing and contact tracing. This study aimed to conceptualize the BICT-enabled epidemic control (BICTEC) and to document its relationships with epidemic curve shaping and epidemic mitigation performance.MethodsDaily COVID-19 incidences were collected from outbreak to Aug. 12, 2020, for nine countries reporting the first outbreak on or before Feb. 1, 2020. Key epidemic curve determinants–peak height (PH), time to peak (TTP), and area under the curve (AUC)–were estimated for each country, and their relationships were analyzed to test if epidemic curves peak quickly at a shorter height. CFR (Case Fatality Rate) and CI (Cumulative Incidence) were compared across the countries to identify relationships between epidemic curve shapes and epidemic mitigation performance.ResultsChina and South Korea had the quickest TTPs (40.70 and 45.37 days since outbreak, respectively) and the shortest PHs (2.95 and 4.65 cases per day, respectively). Sweden, known for its laissez-faire approach, had the longest TTP (120.36) and the highest PH (279.74). Quicker TTPs were correlated with shorter PHs (ρ = 0·896, p = 0·0026) and lower AUCs (0.790, p = 0.0028), indicating that epidemic curves do not follow a flattened trajectory. During the study period, countries with quicker TTPs tended to have lower CIs (ρ = .855, P = .006) and CFRs (ρ = 0.684, P = .061). For example, South Korea, with the second-quickest TTP, reported the second lowest CI and the lowest CFR.ConclusionsCountries that experienced early COVID-19 outbreaks demonstrated the epidemic curves that quickly peak at a shorter height, indicating a departure from the traditional flattened trajectory. South Korea’s BICTEC was found to be at least as effective as most lockdowns in reducing CI and CFR.

IntroductionThe traditional approach to epidemic control has been to slow down the rate of infection while building up healthcare capacity, resulting in a flattened epidemic curve. Advancements in bio-information-communication technology (BICT) have enabled the preemptive isolation of infected cases through efficient testing and contact tracing. This study aimed to conceptualize the BICT-enabled epidemic control (BICTEC) and to document its relationships with epidemic curve shaping and epidemic mitigation performance.MethodsDaily COVID-19 incidences were collected from outbreak to Aug. 12, 2020, for nine countries reporting the first outbreak on or before Feb. 1, 2020. Key epidemic curve determinants–peak height (PH), time to peak (TTP), and area under the curve (AUC)–were estimated for each country, and their relationships were analyzed to test if epidemic curves peak quickly at a shorter height. CFR (Case Fatality Rate) and CI (Cumulative Incidence) were compared across the countries to identify relationships between epidemic curve shapes and epidemic mitigation performance.ResultsChina and South Korea had the quickest TTPs (40.70 and 45.37 days since outbreak, respectively) and the shortest PHs (2.95 and 4.65 cases per day, respectively). Sweden, known for its laissez-faire approach, had the longest TTP (120.36) and the highest PH (279.74). Quicker TTPs were correlated with shorter PHs (ρ = 0·896, p = 0·0026) and lower AUCs (0.790, p = 0.0028), indicating that epidemic curves do not follow a flattened trajectory. During the study period, countries with quicker TTPs tended to have lower CIs (ρ = .855, P = .006) and CFRs (ρ = 0.684, P = .061). For example, South Korea, with the second-quickest TTP, reported the second lowest CI and the lowest CFR.ConclusionsCountries that experienced early COVID-19 outbreaks demonstrated the epidemic curves that quickly peak at a shorter height, indicating a departure from the traditional flattened trajectory. South Korea’s BICTEC was found to be at least as effective as most lockdowns in reducing CI and CFR.

For most of human history, pregnancy and childbirth were very risky; mothers would die in at least 1 in 100 pregnancies.1

Since the average woman would have at least four or five children, the lifetime risk of dying from maternal causes would be at least 1 in 25.2 This was true everywhere.

Thankfully, that’s no longer the case. We’ve made huge strides in not only protecting infants in childbirth and the early stages of their lives, but we’ve also made it much safer for women.

But we’re not done yet. There are still huge inequalities in the risks of pregnancy across the world. Pregnant women in countries like Sierra Leone and Kenya are around 100 times more likely to die during pregnancy or childbirth than those in countries like Norway, Sweden, or Germany.3 But it doesn’t have to be this way. We could save hundreds of thousands of lives a year by closing these gaps.

I’ve compared three scenarios in the chart below to clarify these points.

First, we can see that the situation today is awful. 286,000 women died from maternal causes in 2020.4 That’s 784 deaths per day on average, or one mother dying every two minutes.5

Second, we can consider the very high maternal mortality rates of the past. Particularly good long-term data is available for Finland or Sweden, which shows that in 1750, around 900 women died per 100,000 live births.6 Since there were 135 million births in 2020, I calculate that 1.2 million women would have died from maternal causes that year if these rates hadn’t improved.7 Things are much, much better than they used to be.

Finally, things can still be much better. We know this because some countries have maternal mortality rates that are far lower than the global average. And they all used to be in a similar position to the worst-off countries today. In Europe, the maternal mortality rate was 8 deaths per 100,000 live births in 2020. That’s around 25 times lower than the global average.8 If all countries could achieve the same outcomes as Europe, 11,000 women would have died from maternal causes in 2020 — a small fraction of the 286,000 deaths that occurred.9

Providing the best conditions for women everywhere would reduce the global death toll by 275,000 maternal deaths a year.

The number of confirmed homicides in Sweden over the last 10 years peaked in 2020, when 124 homicides were registered. The number jumped from 2014 to 2015, but has remained just above 100 since. In 2023, 121 homicides were confirmed in Sweden. A significant majority of the victims were men.

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.

TikTok saw an unprecedented increase in popularity during the coronavirus (COVID-19) outbreak in the Nordic region. The largest increase, of up to *** percent was observed among Danish youth. While *** percent of them used TikTik before the COVID-19 outbreak, the corresponding share during the pandemic was ** percent. Overall, TikTok became more popular in Denmark, Sweden, Norway and Finland during the pandemic, regardless of the users’ age.

The rise of TikTok   

TikTok is a Chinese video-sharing social network, initially released in 2018, as Musical.ly. Over the period from 2017 to 2020, the app generated increasingly larger engagement rates, reaching nearly ** million daily active users via iOS as of May 2020 on a global scale. Among the most followed accounts in Norway were the pop duo Marcus & Martinus.

COVID-19 on social media   

As of March 2020, almost all the most popular hashtags on social media in Sweden were related to the coronavirus. In fact, a recent survey showed that especially younger individuals worldwide seemed to rely on social media for updates on the coronavirus that same month . In contrast, the figures were much lower for people aged 55 or older. Nevertheless, social media use generally increased during the pandemic and facilitated the spread of news regarding the coronavirus. In some cases, even false information.

In a survey about attitudes towards gender equality in the presence of International Women’s Day 2022, the question “which issues do you think are the most important facing women and girls in Sweden?” was asked. The three issues most commonly mentioned among respondents in the country were domestic abuse, equal pay, and sexual violence. That domestic abuse is the issue the highest share of people worry about should be seen in relation with the COVID-19 pandemic, when domestic abuse increased in some countries. Equal pay The second most commonly addressed issue among the respondents was that employers should pay women the same as men for the same work. In Sweden in 2021, women’s average earnings were ** percent of men’s in regard to occupation, age, education, working time, and sector. In other words, there is a high degree of equal payment among the genders in Sweden, but a small gap still exists. In Sweden, the gender pay gap was lower than the EU average in 2020. Sexual violence and harassment Sexual violence was the issue that caused the third highest level of worries among the respondents. Indeed, the total number of reported cases of sexual offences increased during the past years. It is difficult to say if there has been a general increase in the actual number of cases, or if the increased awareness around harassment against and abuse of women has contributed to more reporting. Sexual harassment became widely debated after the Me-Too Movement in 2017, and in a survey from 2017 focusing on the movement, ** percent of female respondents stated that they have at least at one point in their life felt that they were sexually harassed or offended.