Internship in Epidemiology/Public Health Research (Master2 student) - GF0919

To be part of the Digital Epidemiology Hub” (PI: Dr. G. Fagherazzi) within the Department of Population Health
1A-B, rue Thomas Edison, Strassen 1445, Luxembourg
23/09/2019 14:34:04
BAckground

The activity on social media platforms continuously generates large amounts of data that 1) can be of high potential for medical research purposes, 2) can help healthcare professionals and scientists to keep being informed about the latest scientific discoveries or remotely follow medical conferences, and 3) can reshape the way patients interact with their peers and exchange health related information and tips to manage their disease.

For medical research, social media data is useful to understand what really matters to patients. For instance, Twitter has already been largely used for research, such as describing the geographic variation of obesity rates in the USA, identifying patterns of use of regular and electronic cigarettes, stress-related tweets, or for disease surveillance (influenza AH1N1, or the Zika virus). It has been suggested that Twitter could also be used for analyzing disease-related symptoms and medications. Social media platforms can be considered as an open digital space with flat role hierarchy for information sharing and development of online communities. Combining social media data with other clinical data is an innovative and complementary approach to what exists in traditional clinical settings, making this new form of research a promising area at the interface between computer science, epidemiology, and medical research.

Objective

Our team aims at identifying innovative digital data that can be leveraged for epidemiological studies. Social media data is currently one of the types of data considered. There is a strong need for a systematic review on the use and potential of such data for medical research purposes

 

Training and research environment

The “Digital Epidemiology Hub” develops a transversal research activity within the Department of Population Health on modern approaches in digital epidemiology, at the frontier with data and computer sciences.

The Master student will directly contribute to the development of a large international initiative to integrate innovate digital data into modern epidemiological studies. He/she will lead a project of systematic review of the literature on the use of social media in medical research. He/She will have to take in charge the literature review, the processing of the data, the data analysis and the writing of the systematic review. He/she will be supervised by Dr. G. Fagherazzi, Research Leader of the Digital Epidemiology Hub. This internship position may lead to a PhD opportunity in digital epidemiology.

What we offer and conditions
  • Students will have the opportunity to work in an interactive and international scientific environment, attend conferences by eminent scientists from abroad, and present their own work during lab meetings.
  • They will receive training in digital epidemiology research and will have the opportunity to gain skills in systematic review and scientific writing.
  • Applicants must be affiliated to their own University.
  • English is mandatory.
  • Master 2 students will receive 500 €/month unless they have their own funding source, e.g.Erasmus grant.

REF.: VD/INTEPHR0919/GF/DoPH
Available for 6-9 months (can be extended according to the specific requirements of the university - full time - Start date : 2020

Related references

  1. Williams SA, Terras MM, Warwick C. What do people study when they study Twitter? Classifying Twitter related academic papers. Journal of Documentation. 2013;69(3):384–410.
  2. Gore RJ, Diallo S, Padilla J. You Are What You Tweet: Connecting the Geographic Variation in America’s Obesity Rate to Twitter Content. PLoS One. 2015 Sep 2;10(9):e0133505.
  3. Jain S, Zhu S-H, Conway M. Exploring Consumer Attitudes towards Hookah, Cigarettes, and Cigars Using Twitter. Tobacco Regulatory Science. 2015;1(3):198–203.
  4. Doan S, Ritchart A, Perry N, Chaparro JD, Conway M. How Do You #relax When You’re #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets. JMIR Public Health and Surveillance. 2017;3(2):e35.
  5. Signorini A, Segre AM, Polgreen PM. The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic. PLoS One. 2011;6(5):e19467.
  6. Stefanidis A, Vraga E, Lamprianidis G, Radzikowski J, Delamater PL, Jacobsen KH, et al. Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts. JMIR Public Health Surveill. 2017 Apr 20;3(2):e22.
  7. Paul MJ, Dredze M. You Are What You Tweet: Analyzing Twitter for Public Health. Association for the Advancement of Artificial Intelligence [Internet]. 2011; Available from: http://www.cs.jhu.edu/~mpaul/files/2011.icwsm.twitter_health.pdf
  8. 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):159–69.