Sweden, which has controversially taken a softer approach to the coronavirus pandemic, said Wednesday that more than one in five people in Stockholm were believed to have developed antibodies to the virus.
An ongoing study by the country’s Public Health Agency showed that 7.3 percent of a sample of randomly selected people in Stockholm – Sweden’s worst-hit region – had antibodies when they were tested in the last week of April.
“The figures reflect the situation in the epidemic earlier in April, since it takes a few weeks before the body’s immune system develops antibodies,” the health agency said in a statement.
Asked about the study during a press conference, state epidemiologist Anders Tegnell said he believed that to date “a little more than 20 percent” had probably contracted the virus in Stockholm – where over a third of Sweden’s confirmed cases have been recorded.
A total of 1,104 tests were analysed.
In other parts of the country, the number of people who tested positive for antibodies was much lower, with 4.2 percent in the far south and 3.7 percent in the region around Gothenburg.
The results also showed the spread was greater among people aged 20-64, of whom 6.7 percent had developed antibodies, compared to 2.7 percent among those aged 65 and up.
Tegnell said this was “a sign of that group actually being pretty good at keeping themselves isolated and protected.”
Meanwhile, around 4.7 percent of those aged zero to 19 had antibodies, which Tegnell said showed “what we have said all along, that this is not where we are seeing a large spread” of the virus.
Sweden has not imposed the extraordinary lockdown measures seen across Europe, but has urged people to follow hygiene and social distancing recommendations and behave responsibly.
The Swedish approach to the novel coronavirus has come under criticism both at home and abroad, particularly as the number of deaths has far exceeded those in neighbouring Nordic countries, which have all imposed more restrictive containment measures.
While it is still unclear whether exposure and development of antibodies means that a person will build up at least some immunity, in theory it should do so and thereby help reduce the spread of the virus.
On Wednesday, Sweden reported a total of 31,523 confirmed cased of the new coronavirus and 3,831 deaths.
Sweden’s strategy is aimed at pressing down the curve so the healthcare system is not overwhelmed, while allowing the rest of society to function as near normally as possible.

Worldwide heat map for total COVID-19 cases by country (as of March 25, 2020).

European heat map for total COVID-19 deaths by country (as of March 25th, 2020).
Italy is the first country facing serious issues and a large number of deaths due to COVID-19 in Europe, followed by Spain, France, Germany, and the United Kingdom [5]. The main issue in all affected countries is that of the health systems’ capabilities and performance. Toward this direction and based on early Italian data about the spread of the disease, all European countries have taken measures aiming at “flattening the curve” [6], meaning to spread the cases—and, consequently, the patients that need to be admitted to the intensive care unit—over a longer period of time.
Said measures mainly consist of flight restrictions, borders closing, shutting down cafes and restaurants, closing of schools, and self-isolation at first and restriction of movement afterwards, with a total lockdown being the last resort, which has unfortunately been taken in several cases, like that of Lombardy and Spain.
The United Kingdom and the Netherlands followed a different approach at first, despite the Imperial College’s Response Team’s reports led by Prof Ferguson [7-9], with many claiming that they were aiming at herd immunity, which also posed several ethical concerns. Even these two countries, however, resorted to some measures and restrictions at the end [10,11].
As Gunther Eysenbach, who first proposed the concept of infodemiology (ie, information epidemiology [12-14]), suggested during the SARS pandemic, the use of population health technologies such as the internet can assist with the detection of diseases during an early stage [15].
Given the serious impact of the novel coronavirus and toward the direction of using new methods and approaches for the nowcasting and forecasting of this pandemic, in this paper, Google Trends data are used to explore the relationship between online interest in COVID-19 and cases and deaths in severely affected European countries (ie, Italy, Spain, France, Germany, and the United Kingdom).
During these times, infodemiology metrics, especially if combined with traditional data, can be an integral part of the surveillance of the virus at the regional level.
References
1. World Health Organization. [2020-03-27]. Rolling updates on coronavirus disease (COVID-19) https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen.
2. Secon H, Woodward A, Mosher D. Business Insider. [2020-03-23]. A comprehensive timeline of the new coronavirus pandemic, from China’s first COVID-19 case to the present https://tinyurl.com/r6johyw.
3. Worldometers [2020-03-27]. Coronavirus. Italy https://www.worldometers.info/coronavirus/country/italy/
4. World Health Organization. 2020. Mar 12, [2020-03-27]. WHO announces COVID-19 outbreak a pandemic http://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19/news/news/2020/3/who-announces-covid-19-outbreak-a-pandemic.
5. Worldometers. [2020-03-19]. COVID-19 coronavirus pandemic https://www.worldometers.info/coronavirus/ [PubMed]
6. Specktor B. LiveScience. 2020. Mar, [2020-04-07]. Coronavirus: what is ‘flattening the curve,’ and will it work? https://www.livescience.com/coronavirus-flatten-the-curve.html.
7. van Elsland SL, O’Hare R. Imperial College London. 2020. Mar 17, [2020-03-27]. COVID-19: Imperial researchers model likely impact of public health measures https://www.imperial.ac.uk/news/196234/covid-19-imperial-researchers-model-likely-impact/
8. Walker PGT, Whittaker C, Watson O, Baguelin M, Ainslie KEC, Bhatia S. Imperial College London. [2020-03-27]. The global impact of COVID-19 and strategies for mitigation and suppression https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-Global-Impact-26-03-2020.pdf.
9. Ferguson N, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, Bhatia S, Boonyasiri A, Cucunubá Z, Cuomo-Dannenburg G, Dighe A, Dorigatti I, Fu H, Gaythorpe K, Green W, Hamlet A, Hinsley W, Okell LC, van Elsland S, Thompson H, Verity R, Volz E, Wang H, Wang Y, Walker PGT, Walters C, Winskill P, Whittaker C, Donnelly CA, Riley S, Ghani AC. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID- 19 mortality and healthcare demand. Imperial College London. 2020 Mar 16; doi: 10.25561/77482. https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf. [CrossRef] [Google Scholar]
10. BBC News. 2020. Mar 26, [2020-03-27]. Coronavirus: UK before and after ‘lockdown’ https://www.bbc.com/news/uk-52051468.
11. Reuters. 2020. Mar 23, [2020-03-27]. Dutch PM Rutte: ban on public gatherings is “intelligent lockdown” https://tinyurl.com/ubx65qg.
12. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res. 2009 Mar 27;11(1):e11. doi: 10.2196/jmir.1157. https://www.jmir.org/2009/1/e11/ [PMC free article] [PubMed] [CrossRef] [Google Scholar]
13. Eysenbach G. Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. Am J Prev Med. 2011 May;40(5 Suppl 2):S154–8. doi: 10.1016/j.amepre.2011.02.006. [PubMed] [CrossRef] [Google Scholar]
14. Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc. 2006:244–8. http://europepmc.org/abstract/MED/17238340. [PMC free article] [PubMed] [Google Scholar]
15. Eysenbach G. SARS and population health technology. J Med Internet Res. 2003;5(2):e14. doi: 10.2196/jmir.5.2.e14. https://www.jmir.org/2003/2/e14/ [PMC free article] [PubMed] [CrossRef] [Google Scholar]
16. Google Trends Explore. [2020-03-26]. https://trends.google.com/trends/explore;
17. Ministero della Salute. [2020-03-19]. Nuovo Coconavirus http://www.salute.gov.it/nuovocoronavirus;
18. Mavragani A, Ochoa G. Google Trends in infodemiology and infoveillance: methodology framework. JMIR Public Health Surveill. 2019 May 29;5(2):e13439. doi: 10.2196/13439. https://publichealth.jmir.org/2019/2/e13439/ [PMC free article] [PubMed] [CrossRef] [Google Scholar]
19. Mavragani A. Infodemiology and infoveillance: a scoping review [accepted manuscript] J Med Internet Res. 2020 [Google Scholar]
20. Mavragani A, Ochoa G, Tsagarakis KP. Assessing the methods, tools, and statistical approaches in Google Trends research: systematic review. J Med Internet Res. 2018 Nov 06;20(11):e270. doi: 10.2196/jmir.9366. https://www.jmir.org/2018/11/e270/ [PMC free article] [PubMed] [CrossRef] [Google Scholar]
21. Poletto C, Boëlle P-Y, Colizza V. Risk of MERS importation and onward transmission: a systematic review and analysis of cases reported to WHO. BMC Infect Dis. 2016 Aug 25;16(1):448. doi: 10.1186/s12879-016-1787-5. https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-016-1787-5. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
22. Mavragani A, Ochoa G. The internet and the anti-vaccine movement: tracking the 2017 EU measles outbreak. BDCC. 2018 Jan 16;2(1):2. doi: 10.3390/bdcc2010002. [CrossRef] [Google Scholar]
23. Du J, Tang L, Xiang Y, Zhi D, Xu J, Song H-Y, Tao C. Public perception analysis of Tweets during the 2015 measles outbreak: comparative study using convolutional neural network models. J Med Internet Res. 2018 Jul 09;20(7):e236. doi: 10.2196/jmir.9413. https://www.jmir.org/2018/7/e236/ [PMC free article] [PubMed] [CrossRef] [Google Scholar]
24. Hossain L, Kam D, Kong F, Wigand RT, Bossomaier T. Social media in Ebola outbreak. Epidemiol Infect. 2016 Mar 04;144(10):2136–2143. doi: 10.1017/s095026881600039x. [PubMed] [CrossRef] [Google Scholar]
25. van Lent LG, Sungur H, Kunneman FA, van de Velde B, Das E. Too far to care? Measuring public attention and fear for Ebola using Twitter. J Med Internet Res. 2017 Jun 13;19(6):e193. doi: 10.2196/jmir.7219. https://www.jmir.org/2017/6/e193/ [PMC free article] [PubMed] [CrossRef] [Google Scholar]
26. Bentley RA, Ormerod P. Social versus independent interest in ‘bird flu’ and ‘swine flu’. PLoS Curr. 2009 Sep 03;1:RRN1036. doi: 10.1371/currents.rrn1036. doi: 10.1371/currents.rrn1036. [PMC free article] [PubMed] [CrossRef] [CrossRef] [Google Scholar]
27. Farhadloo M, Winneg K, Chan MS, Hall Jamieson K, Albarracin D. Associations of topics of discussion on Twitter with survey measures of attitudes, knowledge, and behaviors related to Zika: probabilistic study in the United States. JMIR Public Health Surveill. 2018 Feb 09;4(1):e16. doi: 10.2196/publichealth.8186. https://publichealth.jmir.org/2018/1/e16/ [PMC free article] [PubMed] [CrossRef] [Google Scholar]
28. Chen S, Xu Q, Buchenberger J, Bagavathi A, Fair G, Shaikh S, Krishnan S. Dynamics of health agency response and public engagement in public health emergency: a case study of CDC Tweeting patterns during the 2016 Zika epidemic. JMIR Public Health Surveill. 2018 Nov 22;4(4):e10827. doi: 10.2196/10827. https://publichealth.jmir.org/2018/4/e10827/ [PMC free article] [PubMed] [CrossRef] [Google Scholar]
29. BBC News. 2020. Feb 07, [2020-03-27]. Li Wenliang: coronavirus kills Chinese whistleblower doctor https://www.bbc.com/news/world-asia-china-51403795.
30. Withnall A. Independent. 2020. Mar 18, [2020-03-27]. Coronavirus: China expels 13 American reporters amid ‘unparalleled global crisis’ of pandemic https://tinyurl.com/rmq7vkp.
31. Twitter. World Health Organization (WHO) (@WHO) https://twitter.com/who/status/1217043229427761152?lang=en.32. The Guardian. [2020-03-27]. Coronavirus live news: record rise in Italy death toll takes total to 9,134, as France extends lockdown by two weeks https://tinyurl.com/w9cw2x7.
[…] Covid-19 : Sweden – more than one in five people… […]