Early warnings of COVID-19 outbreaks across Europe from social media
Authors: Pietro Panzarasa
Milena Lopreite
Michelangelo Puliga
Massimo Riccaboni
Journal: Scientific Reports (2021)
Discipline: Multidisciplinary
Keywords: Twitter data; early-warning signals; COVID-19 outbreaks; Europe; winter season 2019-2020
Affiliations: Department of Economics, Statistics and Finance, University of Calabria, Calabria, Italy., Institute of Management, Scuola Superiore Sant'Anna, Pisa, Italy., School of Business and Management, Queen Mary University of London, London, UK. ., IMT School for Advanced Studies, Lucca, Italy., Linkalab Computational Laboratory, Cagliari, Italy.

Abstract:  We analyze data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2019-2020, before the first public announcements of local sources of infection were made. We show evidence that unexpected levels of concerns about cases of pneumonia were raised across a number of European countries. Whistleblowing came primarily from the geographical regions that eventually turned out to be the key breeding grounds for infections. These findings point to the urgency of setting up an integrated digital surveillance system in which social media can help geo-localize chains of contagion that would otherwise proliferate almost completely undetected. 

Retracted: No

PubPeer comments:
 The authors claim that they detected early evidence of COVID-19 outbreaks in Europe thanks to tweets. I claim that 1) they detected discussion about the epidemic in China; 2) they detected which regions in Europe are the most populous. Point 1): The authors did make a few adjustments to avoid selecting tweets directly commenting on pieces of news. In particular, they removed all remaining tweets that still contained the word u201cCoronavirusu201d, u201cChinau201d, or u201cCOVIDu201d. But many other words are linked to the epidemic in China, like  Wuhan ,  market , etc. The authors would need to provide the text of the `2716 ` tweets that they selected, for readers to be able to check that these tweets are indeed not commenting on pieces of news. Point 2): The maps showing numbers of tweets per regions are not standardized by the corresponding population sizes of the regions. Unexpectedly, the bigger ones have the largest number of tweets (e.g. England in Fig.3), and so do the most populated ones (e.g. Paris, Berlin, Madrid areas). , 
 A short sample of the tweets detected were like the following:  Avoir une pneumonie le 31 du du00e9cembre pas ouf   Grosse bronchite aiguu00eb au bord de la pneumonie, dose d antibiotiques monstrueuses sans amu00e9lioration .. energie plat, quintes de toux.. bon j ai connue des meilleures vacances ..  le swag est u00e9ternel, la pneumonie temporaireu201d u201cet bah moi qui pensais que ma pneumonie m avait laissu00e9 tranquille et bah pas du tout. je me retrouve au cracher mes poumons grrr j aime pas u00e7au201d  Mdrrr c est pas une bronchite qu ils vont avoir mais une pneumonie  Von einer Volumengabe ging ich schon selbstredend aus! Bzw vom Vorhandensein einer Solchen...Volumentherapie wird Herr*Frau Dokt.ja hoffentlich zumindest gemacht haben bei ihrer Pneumonie  Estoy empapada abajo del aire del sarmiento, les hablo ahora porque mau00f1ana voy a estar internada con neumonu00eda.  Nos agarr la lluvia y terminamos empapados lpm neumonu00eca ya te siento  mau00f1ana me levanto con anginas, fiebre, neumonu00eda y algo mu00e1s seguro  I won t reach sixty. I predicted in 1999 saying to the GF I had at the time I would not reach fifty. I reached fifty and nearly died six weeks later of #pneumonia must be some sort of nostradamus of misery  as you can see the text (we do not report here the full tweets or the authors for privacy reasons) is not about China, nor Wuhan, nor market etc. (btw the word Wuhan, Hubei, and others were among the ones removed, and also all links to news, all popular users, all news agencies ...). About the question of population. What counts most is not the absolute number of tweets per region that is surely, and trivially related to the population size, but the relative variation with the previous years. This ratio ( X1- X0 ) X0 is by definition normalized as the effect of the population size is on the denominator. In other words, the relevant geographical clues were on the relative variation not on the absolute size. As a final word, the effects of the Chinese news were present in Europe since the end of December but the change in the slope of the signal appeared in different moments for different countries, an effect that, in January, cannot be trivially explained by a surge of news of Chinese pneumonia. , 
 Dear Pietro Panzarasa, Thanks for your reply. Regarding 1): you are providing here a few examples, but this is not proving that most tweets are not about the Wuhan epidemic. Could you deposit the list of texts on a public repository? You write here that you removed the words  Hubei ,  Wuhan as well, but this is not specified in the Methods of the article, which makes your study non-reproducible. Regarding 2): The relative chance in the number of tweets would need to be also controlled by the changes in the total number of tweets on Twitter, ideally with values specific to each geographic region.  Dry cough could also be a control, but the map of relative change for this term does not seem to be provided. , 
 Correction for #3 : dry cough would not be a control as it is another symptom; the control should be word unrelated to COVID-19. ,