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Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

May 25, 2021

Journal of Medical Internet Research This paper is in the following e-collection/theme issue: January 11, 2021 . Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study Authors of this article: 2Computer Science, University of New Mexico, Albuquerque, NM, United States Corresponding Author: Analytics, Intelligence, and Technology Abstract Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors. J Med Internet Res 2021;23(5):e27059 Principal Findings The ongoing COVID-19 outbreak clearly illustrates the need for real-time information gathering to assess evolving beliefs and behaviors that directly impact disease spread. Historically, such information would be gathered using survey methods [ 5 , 7 , 31 ], which are time-consuming, expensive, and typically lack the ability to measure temporal and spatial variation [ 32 ]. One proposed partial solution is to use internet data (eg, search query patterns and social media data), which have been shown to correspond to disease incidence in emergent infectious disease outbreaks [ 23 , 33 - 35 ], individual risk perception [ 1 , 36 , 37 ], and risk communication [ 38 ], and have been used to identify specific health behaviors [ 15 ]. During the early stages of the current COVID-19 pandemic, social media data have been used to monitor the top concerns of individuals [ 39 , 40 ], characterize COVID-19 awareness [ 41 ], compare social connectedness and COVID-19 hot spots [ 42 ], monitor misinformation [ 40 , 43 - 45 ], and rapidly disseminate information [ 46 ]. Last, social media has been used as an information gathering platform during periods of uncertain information. Disease emergence is a context wherein disease risks, transmission, and treatment may be largely unclear [ 46 ]. With this context in mind, we address our findings with respect to each research question below. What behaviors related to COVID-19 are discussed on social media websites, like Twitter? We find that there are a wide variety of behaviors discussed on social media, including mask-wearing, hygiene (eg, handwashing), testing availability and experiences, and social distancing practices. Prior work has found evidence that mask-wearing and limited mobility were behaviors adopted to reduce disease spread during SARS [ 5 ] and that handwashing would be commonly implemented by individuals during a hypothetical pandemic influenza [ 47 ]. This prior work, however, has relied on surveys to obtain data about the behaviors that individuals implement. The use of social media to complement such work would improve both the richness and the temporal and geographic scope of the data available. Some of the identified tweets show evidence of sensitive topics. For example, we found 53 tweets related to individuals’ mental health. Prior research has found that social media can be used to identify individuals with a variety of mental health concerns, including depression [ 48 ] and suicide [ 14 ]. As there is considerable work emerging about the substantial mental health impacts of COVID-19 (eg, increases in domestic violence [ 49 ] as well as depression and anxiety [ 50 ]), this could prove to be an important avenue for future work in this field. Last, we found a small number of tweets (n=49) about vaccination related to COVID-19, of which roughly a third (n=18) showed a negative attitude. Importantly, this study was conducted prior to the authorization of any vaccines in the United States. All of the tweets considered here discuss either vaccine development or a hypothetical COVID-19 vaccine. Prior research has found similarly negative tweets during the emergence of Zika [ 51 ] and the H1N1 influenza pandemic [ 52 ]. Future work analyzing these data could provide additional insight into specific reasons that populations may be hesitant to receive the COVID-19 vaccine and could inform targeted public health messaging. How do patterns in behaviors change geospatially and temporally in the United States? As expected, the patterns in tweets classified as social distancing and shelter-in-place followed extremely similar trends. These patterns corresponded to important real-world events during the outbreak, suggesting that individuals were responding to actual events and some were describing their own personal behavior. We found, however, that tweets classified as personal mentions represented a very small subset of social distancing and shelter-in-place tweets. This is not unexpected, given that prior work has shown that personal mentions of health may be extremely uncommon [ 20 ]. How do these trends compare to other data streams, like mobility data sets, that have also shown promise in COVID-19 modeling efforts? Despite the lack of a temporal signal in tweets labeled as personal and social distancing, there was a stronger signal when comparing classified data to Descartes Labs’ mobility data. We observed meaningful regional differences between states and saw that, in general, the peak number of tweets about social distancing happened within a few weeks of the actual measured minimum in mobility. This suggests that social media data may be used as a proxy for sensor data in appropriately data-rich contexts. Recent work using geotagged Twitter data to create social networks and analyze social distancing in the context of policy decisions found similar relationships and supports this finding [ 53 ]. Limitations There are a number of limitations to consider in this work. The first is that, as mentioned above, it is known that social media data are biased in a number of ways, including demographically, and that bias differs by geographic areas [ 18 ]. Further, personal mentions of health-related information on Twitter are rare [ 19 ]. These are known limitations of using internet data and could potentially explain the variations in correlation we observed between social distancing posts and actual mobility data. Importantly, however, it is difficult to assess this without extensive prospective surveys conducted at the same time as tweet collection. Our observed wide range in correlations between the proportion of social distancing tweets and actual COVID-19 cases in individual states is an example of the ecological fallacy. State-level COVID-19 cases represent an aggregate measure of a state’s behavior, while tweets represent individual actions and observations. The available data do not allow us to probe the reasons for the variation, but a number of possible factors could be at play. Individuals’ social distancing thoughts at a specific moment in time will be influenced by contextual information about other aspects of their lives. For example, people that tweet in support of social distancing may have in-person jobs or be in high-risk groups, which could motivate them to use social media platforms to voice support for public health measures. The stronger correlation with mobility outcomes is expected by this same argument because mobility is more directly representative of individual actions. Additionally, tweeting norms could be systematically different across the country (eg, people in different states might be more or less likely to talk about social distancing based on the policies in place and the perceived threat of COVID-19). It is also possible that there are differences in which individuals use Twitter and have geolocation services enabled in different states. In an operational context, it is hugely important to combine internet data with traditional data streams in order to provide a more complete picture of an evolving scenario. Future work should focus on targeted studies to better understand potential bias. An additional known source of bias comes from imperfect classification. Our classifiers performed similarly to other classifiers used to identify health behaviors [ 15 ], but were clearly not perfect. To account for known classifier bias, we used an adjusted bootstrapping method from Daughton and Paul [ 25 ], which generates accurate confidence intervals despite classifier error. We validated our work using mobility data from Descartes Labs. However, there are a number of mobility data sources available [ 54 ]. Prior work indicates that these data have similar patterns [ 54 ], but it is possible that using a different source would produce slightly different validation results. Conclusions Behavior changes and policy decisions that occur early within an outbreak have the largest effects on disease dynamics [ 55 , 56 ]. Real-time conversations about health behaviors, in addition to other behavioral data sources such as mobility metrics or media consumption (eg, home television viewing [ 55 ]), could help improve overall knowledge and policy decisions in the early stages of an epidemic and could better capture dynamic changes caused by uncoordinated behavioral change. Using such data has the unique capability to inform public health decisions as an outbreak emerges, especially with respect to public health communication. The World Health Organization suggests a communication checklist to prepare for and minimize morbidity and mortality in the event of a pandemic [ 57 , 58 ]. The checklist emphasizes building public trust through early communication, even with incomplete information, and evaluating the impact of communication programs to assess whether recommendations are being followed. The use of social media streams as a simultaneous real-time measure of public sentiment toward messaging and a dynamic evaluation tool of communication effectiveness could be invaluable in minimizing effects from a future disease outbreak. Acknowledgments ARD, CDS, and DG created the labeling schema. DG, NP, TP, ARD, CWR, GF, and NYVC collected and analyzed the Twitter data. ARD, CDS, DG, IC, GN, and NM labeled the tweets. ARD and CWR built the supervised learning models, and ARD implemented the classifier-adjusted bootstrapped sampling. MB collected the mobility data and created several figures. ARD, CDS, and MB wrote the initial paper. All authors provided critical revisions to the paper. ARD led the project. Research support was provided by the Laboratory Directed Research and Development program of Los Alamos National Laboratory (project No. 20200721ER) and the US Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the US Department of Energy (Contract No. 89233218CNA000001). The Los Alamos National Laboratory Review & Approval System reporting number is LA-UR-21-20074. Conflicts of Interest References Taylor S. The Psychology of Pandemics: Preparing for the Next Global Outbreak of Infectious Disease. Newcastle upon Tyne, UK: Cambridge Scholars Publishing; 2019. Bults M, Beaujean DJ, Richardus JH, Voeten HA. Perceptions and behavioral responses of the general public during the 2009 influenza A (H1N1) pandemic: A systematic review. Disaster Med Public Health Prep 2015 Apr;9(2):207-219. [ CrossRef ] [ Medline ] Douglas PK, Douglas DB, Harrigan DC, Douglas KM. Preparing for pandemic influenza and its aftermath: Mental health issues considered. Int J Emerg Ment Health 2009;11(3):137-144. [ Medline ] Shultz JM, Espinel Z, Flynn BW, Hoffman Y, Cohen RE. Deep Prep: All-Hazards Disaster Behavioral Health Training. Miami, FL: Miller School of Medicine, University of Miami; 2008. Lau JTF, Yang X, Pang E, Tsui HY, Wong E, Wing YK. SARS-related perceptions in Hong Kong. Emerg Infect Dis 2005 Mar;11(3):417-424 [ FREE Full text ] [ CrossRef ] [ Medline ] MacDonald PDM, Holden EW. Zika and public health: Understanding the epidemiology and information environment. Pediatrics 2018 Feb 01;141(Supplement 2):S137-S145. [ CrossRef ] Darrow W, Bhatt C, Rene C, Thomas L. Zika virus awareness and prevention practices among university students in Miami: Fall 2016. Health Educ Behav 2018 Dec;45(6):967-976. [ CrossRef ] [ Medline ] Mendoza C, Jaramillo G, Ant TH, Power GM, Jones RT, Quintero J, et al. An investigation into the knowledge, perceptions and role of personal protective technologies in Zika prevention in Colombia. PLoS Negl Trop Dis 2020 Jan;14(1):e0007970 [ FREE Full text ] [ CrossRef ] [ Medline ] White RW, Horvitz E. From health search to healthcare: Explorations of intention and utilization via query logs and user surveys. J Am Med Inform Assoc 2014;21(1):49-55 [ FREE Full text ] [ CrossRef ] [ Medline ] Coogan S, Sui Z, Raubenheimer D. Gluttony and guilt: Monthly trends in internet search query data are comparable with national-level energy intake and dieting behavior. Palgrave Commun 2018 Jan 9;4(1):1-9. [ CrossRef ] Ayers JW, Ribisl KM, Brownstein JS. Tracking the rise in popularity of electronic nicotine delivery systems (electronic cigarettes) using search query surveillance. Am J Prev Med 2011 Apr;40(4):448-453. [ CrossRef ] [ Medline ] Eichstaedt JC, Schwartz HA, Kern ML, Park G, Labarthe DR, Merchant RM, et al. Psychological language on Twitter predicts county-level heart disease mortality. Psychol Sci 2015 Feb;26(2):159-169 [ FREE Full text ] [ CrossRef ] [ Medline ] Paul MJ, Dredze M. You are what you tweet: Analyzing Twitter for public health. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. 2011 Presented at: Fifth International AAAI Conference on Weblogs and Social Media; July 17-21, 2011; Barcelona, Spain   URL: https://ojs.aaai.org/index.php/ICWSM/article/view/14137/13986 McClellan C, Ali MM, Mutter R, Kroutil L, Landwehr J. Using social media to monitor mental health discussions - Evidence from Twitter. J Am Med Inform Assoc 2017 May 01;24(3):496-502 [ FREE Full text ] [ CrossRef ] [ Medline ] Daughton AR, Paul MJ. Identifying protective health behaviors on Twitter: Observational study of travel advisories and Zika virus. J Med Internet Res 2019 May 13;21(5):e13090 [ FREE Full text ] [ CrossRef ] [ Medline ] Ramanadhan S, Mendez SR, Rao M, Viswanath K. Social media use by community-based organizations conducting health promotion: A content analysis. BMC Public Health 2013 Dec 05;13:1129 [ FREE Full text ] [ CrossRef ] [ Medline ] Carrotte ER, Prichard I, Lim MSC. "Fitspiration" on social media: A content analysis of gendered images. J Med Internet Res 2017 Mar 29;19(3):e95 [ FREE Full text ] [ CrossRef ] [ Medline ] Mislove A, Lehmann S, Ahn Y, Onnela J, Rosenquist J. Understanding the demographics of Twitter users. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. 2011 Presented at: Fifth International AAAI Conference on Weblogs and Social Media; July 17-21, 2011; Barcelona, Spain   URL: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewFile/2816/3234 Daughton AR, Chunara R, Paul MJ. Comparison of social media, syndromic surveillance, and microbiologic acute respiratory infection data: Observational study. JMIR Public Health Surveill 2020 Apr 24;6(2):e14986 [ FREE Full text ] [ CrossRef ] [ Medline ] Engle S, Stromme J, Zhou A. Staying at home: Mobility effects of COVID-19. SSRN J 2020:1-16 (forthcoming). [ CrossRef ] Buckee CO, Balsari S, Chan J, Crosas M, Dominici F, Gasser U, et al. Aggregated mobility data could help fight COVID-19. Science 2020 Apr 10;368(6487):145-146. [ CrossRef ] [ Medline ] Chen E, Lerman K, Ferrara E. Tracking social media discourse about the COVID-19 pandemic: Development of a public coronavirus Twitter data set. JMIR Public Health Surveill 2020 May 29;6(2):e19273 [ FREE Full text ] [ CrossRef ] [ Medline ] Lamb A, Paul MJ, Dredze M. Separating fact from fear: Tracking flu infections on Twitter. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2013 Presented at: 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; June 9-14, 2013; Atlanta, GA p. 789-795. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011 Nov;12:2825-2830 [ FREE Full text ] [ CrossRef ] Daughton AR, Paul MJ. Constructing accurate confidence intervals when aggregating social media data for public health monitoring. In: Proceedings of the International Workshop on Health Intelligence (W3PHAI 2019). 2019 Presented at: International Workshop on Health Intelligence (W3PHAI 2019); January 27-February 1, 2019; Honolulu, HI. Data for mobility changes in response to COVID-19. GitHub. Santa Fe, NM: Descartes Labs   URL: https://github.com/descarteslabs/DL-COVID-19 [accessed 2021-05-06] Warren MS, Skillman SW. Mobility changes in response to COVID-19. ArXiv. Preprint posted online on March 31, 2020. [ FREE Full text ] Coronavirus (Covid-19) data in the United States. GitHub. New York, NY: The New York Times   URL: https://github.com/nytimes/covid-19-data [accessed 2021-05-06] State “shelter-in-place” and “stay-at-home” orders. FINRA. URL: https://www.finra.org/rules-guidance/key-topics/covid-19/shelter-in-place [accessed 2020-12-23] Mervosh S, Lu D, Swales V. See which states and cities have told residents to stay at home. The New York Times. 2020 Apr 20. URL: https://www.nytimes.com/interactive/2020/us/coronavirus-stay-at-home-order.html [accessed 2021-01-05] Chandrasekaran N, Marotta M, Taldone S, Curry C. Perceptions of community risk and travel during pregnancy in an area of Zika transmission. Cureus 2017 Jul 26;9(7):e1516 [ FREE Full text ] [ CrossRef ] [ Medline ] Blaikie N. Designing Social Research: The Logic of Anticipation. 2nd edition. Cambridge, UK: Polity Press; 2009. Chan EH, Sahai V, Conrad C, Brownstein JS. Using web search query data to monitor dengue epidemics: A new model for neglected tropical disease surveillance. PLoS Negl Trop Dis 2011 May;5(5):e1206. [ CrossRef ] [ Medline ] Culotta A. Towards detecting influenza epidemics by analyzing Twitter messages. In: Proceedings of the First Workshop on Social Media Analytics (SOMA '10). 2010 Presented at: First Workshop on Social Media Analytics (SOMA '10); July 25, 2010; Washington, DC p. 155-122. [ CrossRef ] Watad A, Watad S, Mahroum N, Sharif K, Amital H, Bragazzi NL, et al. Forecasting the West Nile virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior. JMIR Public Health Surveill 2019 Feb 28;5(1):e9176 [ FREE Full text ] [ CrossRef ] [ Medline ] Hassan MS, Halbusi HA, Najem A, Razali A, Williams KA, Mustamil NM. Impact of risk perception on trust in government and self-efficiency during COVID-19 pandemic: Does social media content help users adopt preventative measures? Research Square. Preprint posted online on July 16, 2020. [ CrossRef ] Oh SH, Lee SY, Han C. The effects of social media use on preventive behaviors during infectious disease outbreaks: The mediating role of self-relevant emotions and public risk perception. Health Commun 2020 Feb 16:1-10. [ CrossRef ] [ Medline ] Ding H, Zhang J. Social media and participatory risk communication during the H1N1 flu epidemic: A comparative study of the United States and China. China Media Res 2010;6(4):80-91 [ FREE Full text ] Abd-Alrazaq A, Alhuwail D, Househ M, Hamdi M, Shah Z. Top concerns of tweeters during the COVID-19 pandemic: Infoveillance study. J Med Internet Res 2020 Apr 21;22(4):e19016 [ FREE Full text ] [ CrossRef ] [ Medline ] Singh L, Bansal S, Bode L, Budakb C, Chic G, Kawintiranona K, et al. A first look at COVID-19 information and misinformation sharing on Twitter. ArXiv. Preprint posted online on March 31, 2020. [ FREE Full text ] Saad M, Hassan M, Zaffar F. Towards characterizing COVID-19 awareness on Twitter. ArXiv. Preprint posted online on May 21, 2020. [ FREE Full text ] Bailey M, Cao R, Kuchler T, Stroebel J, Wong A. Social connectedness: Measurement, determinants, and effects. J Econ Perspect 2018 Aug 01;32(3):259-280. [ CrossRef ] Ahmed W, Vidal-Alaball J, Downing J, López Seguí F. COVID-19 and the 5G conspiracy theory: Social network analysis of Twitter data. J Med Internet Res 2020 May 06;22(5):e19458 [ FREE Full text ] [ CrossRef ] [ Medline ] Broniatowski DA, Paul MJ, Dredze M. National and local influenza surveillance through Twitter: An analysis of the 2012-2013 influenza epidemic. PLoS One 2013;8(12):e83672 [ FREE Full text ] [ CrossRef ] [ Medline ] Gerts D, Shelley CD, Parikh N, Pitts T, Watson Ross C, Fairchild G, et al. "Thought I'd share first" and other conspiracy theory tweets from the COVID-19 infodemic: Exploratory study. JMIR Public Health Surveill 2021 Apr 14;7(4):e26527 [ FREE Full text ] [ CrossRef ] [ Medline ] Chan AKM, Nickson CP, Rudolph JW, Lee A, Joynt GM. Social media for rapid knowledge dissemination: Early experience from the COVID-19 pandemic. Anaesthesia 2020 Dec;75(12):1579-1582 [ FREE Full text ] [ CrossRef ] [ Medline ] Sadique MZ, Edmunds WJ, Smith RD, Meerding WJ, de Zwart O, Brug J, et al. Precautionary behavior in response to perceived threat of pandemic influenza. Emerg Infect Dis 2007 Sep;13(9):1307-1313 [ FREE Full text ] [ CrossRef ] [ Medline ] De Choudhury CM, Gamon M, Counts S, Horvitz S. Predicting depression via social media. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. 2013 Presented at: Seventh International AAAI Conference on Weblogs and Social Media; July 8-11, 2013; Boston, MA   URL: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/viewFile/6124/6351 Kofman YB, Garfin DR. Home is not always a haven: The domestic violence crisis amid the COVID-19 pandemic. Psychol Trauma 2020 Aug;12(S1):S199-S201 [ FREE Full text ] [ CrossRef ] [ Medline ] Vindegaard N, Benros ME. COVID-19 pandemic and mental health consequences: Systematic review of the current evidence. Brain Behav Immun 2020 Oct;89:531-542 [ FREE Full text ] [ CrossRef ] [ Medline ] Ghenai A, Mejova Y. Catching Zika fever: Application of crowdsourcing and machine learning for tracking health misinformation on Twitter. In: Proceedings of the 2017 IEEE International Conference on Healthcare Informatics (ICHI). 2017 Presented at: 2017 IEEE International Conference on Healthcare Informatics (ICHI); August 23-26, 2017; Park City, UT p. 518. [ CrossRef ] Salathé M, Khandelwal S. Assessing vaccination sentiments with online social media: Implications for infectious disease dynamics and control. PLoS Comput Biol 2011 Oct;7(10):e1002199 [ FREE Full text ] [ CrossRef ] [ Medline ] Porcher S, Renault T. Social distancing beliefs and human mobility: Evidence from Twitter. ArXiv. Preprint posted online on August 10, 2020. [ FREE Full text ] [ CrossRef ] Huang X, Li Z, Jiang Y, Ye X, Deng C, Zhang J, et al. The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the US during the COVID-19 pandemic. Int J Digit Earth 2021 Feb 17;14(4):424-442. [ CrossRef ] Schwarzinger M, Flicoteaux R, Cortarenoda S, Obadia Y, Moatti J. Low acceptability of A/H1N1 pandemic vaccination in French adult population: Did public health policy fuel public dissonance? PLoS One 2010 Apr 16;5(4):e10199 [ FREE Full text ] [ CrossRef ] [ Medline ] Springborn M, Chowell G, MacLachlan M, Fenichel EP. Accounting for behavioral responses during a flu epidemic using home television viewing. BMC Infect Dis 2015 Jan 23;15:21 [ FREE Full text ] [ CrossRef ] [ Medline ] World Health Organization, Department of Communicable Disease Surveillance and Response. WHO Guidelines for Epidemic Preparedness and Response to Measles Outbreaks. Geneva, Switzerland: World Health Organization; 1999 May. URL: http://www.who.int/csr/resources/publications/measles/whocdscsrisr991.pdf [accessed 2016-07-27] World Health Organization, Department of Communicable Disease Surveillance and Response, Global Influenza Programme. WHO Checklist for Influenza Pandemic Preparedness Planning. Geneva, Switzerland: World Health Organization; 2005. URL: https://www.who.int/influenza/resources/documents/FluCheck6web.pdf?ua=1 [accessed 2020-05-06] ‎ API: application programming interface Edited by C Basch; submitted 11.01.21; peer-reviewed by X Zhou, L Guo, Z Jin; comments to author 01.03.21; revised version received 08.03.21; accepted 17.04.21; published 25.05.21

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