Skip to main navigation menu Skip to main content Skip to site footer

Original article

Vol. 147 No. 1314 (2017)

Precision global health in the digital age

Cite this as:
Swiss Med Wkly. 2017;147:w14423


Precision global health is an approach similar to precision medicine, which facilitates, through innovation and technology, better targeting of public health interventions on a global scale, for the purpose of maximising their effectiveness and relevance. Illustrative examples include: the use of remote sensing data to fight vector-borne diseases; large databases of genomic sequences of foodborne pathogens helping to identify origins of outbreaks; social networks and internet search engines for tracking communicable diseases; cell phone data in humanitarian actions; drones to deliver healthcare services in remote and secluded areas. Open science and data sharing platforms are proposed for fostering international research programmes under fair, ethical and respectful conditions. Innovative education, such as massive open online courses or serious games, can promote wider access to training in public health and improving health literacy. The world is moving towards learning healthcare systems. Professionals are equipped with data collection and decision support devices. They share information, which are complemented by external sources, and analysed in real time using machine learning techniques. They allow for the early detection of anomalies, and eventually guide appropriate public health interventions. This article shows how information-driven approaches, enabled by digital technologies, can help improving global health with greater equity.


  1. Wernli D, Tanner M, Kickbusch I, Escher G, Paccaud F, Flahault A. Moving global health forward in academic institutions. J Glob Health. 2016;6(1):010409.
  2. Khoury MJ, Iademarco MF, Riley WT. Precision Public Health for the Era of Precision Medicine. Am J Prev Med. 2016;50(3):398–401.
  3. WHO. Global Action Plan for the Prevention and Control of NCDs 2013-2020 2015 [updated 2015-10-05 03:00:00]. Available from:
  4. Chuang TW, Wimberly MC. Remote sensing of climatic anomalies and West Nile virus incidence in the northern Great Plains of the United States. PLoS One. 2012;7(10):e46882.
  5. Kalluri S, Gilruth P, Rogers D, Szczur M. Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review. PLoS Pathog. 2007;3(10):e361–71.
  6. Ruiz-Moreno D. Assessing Chikungunya risk in a metropolitan area of Argentina through satellite images and mathematical models. BMC Infect Dis. 2016;16(1):49.
  7. Buchholz U, Bernard H, Werber D, Böhmer MM, Remschmidt C, Wilking H, et al. German outbreak of Escherichia coli O104:H4 associated with sprouts. N Engl J Med. 2011;365(19):1763–70.
  8. Bergholz TM, Moreno Switt AI, Wiedmann M. Omics approaches in food safety: fulfilling the promise? Trends Microbiol. 2014;22(5):275–81.
  9. Worldwide Antimalarial Resistance Network | Home 2016. Available from:
  10. Adjuik MA, Allan R, Anvikar AR, Ashley EA, Ba MS, Barennes H, et al., WorldWide Antimalarial Resistance Network (WWARN) AS-AQ Study Group. The effect of dosing strategies on the therapeutic efficacy of artesunate-amodiaquine for uncomplicated malaria: a meta-analysis of individual patient data. BMC Med. 2015;13(1):66.
  11. Achan J, Adam I, Arinaitwe E, Ashley EA, Awab GR, Ba MS, et al., WorldWide Antimalarial Resistance Network (WWARN) DP Study Group. The effect of dosing regimens on the antimalarial efficacy of dihydroartemisinin-piperaquine: a pooled analysis of individual patient data. PLoS Med. 2013;10(12):e1001564, discussion e1001564. Correction in: PLoS Med. 2013 Dec;10(12). doi:10.1371/annotation/3db421e4-3e27-4442-8092-2ad1b778f371.
  12. WHO. Guidelines for the treatment of malaria. Third edition 2016 [updated 2016-08-26 15:53:07]. Available from:
  13. Homepage. Global Alliance for Genomics and Health 2016. Available from:
  14. O’Donovan J, Bersin A. Controlling Ebola through mHealth strategies. Lancet Glob Health. 2015;3(1):e22.
  15. Dougherty E. Mobilizing a Revolution: How cellphones are transforming public health 2012. Available from:
  16. Milusheva S. Less Bite for your Buck: Using Cell Phone Data to Target Disease Prevention (Preliminary Draft) 2016. Available from:
  17. Viboud C, Boëlle PY, Carrat F, Valleron AJ, Flahault A. Prediction of the spread of influenza epidemics by the method of analogues. Am J Epidemiol. 2003;158(10):996–1006.
  18. Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One. 2010;5(11):e14118.
  19. McIver DJ, Brownstein JS. Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLOS Comput Biol. 2014;10(4):e1003581.
  20. Carneiro HA, Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis. 2009;49(10):1557–64.
  21. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012–4.
  22. Cook S, Conrad C, Fowlkes AL, Mohebbi MH. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One. 2011;6(8):e23610.
  23. Butler D. When Google got flu wrong. Nature. 2013;494(7436):155–6.
  24. Paul MJ, Dredze M, Broniatowski D. Twitter improves influenza forecasting. PLoS Curr. 2014;6.
  25. Hickmann KS, Fairchild G, Priedhorsky R, Generous N, Hyman JM, Deshpande A, et al. Forecasting the 2013-2014 influenza season using Wikipedia. PLOS Comput Biol. 2015;11(5):e1004239.
  26. Haidari LA, Brown ST, Ferguson M, Bancroft E, Spiker M, Wilcox A, et al. The economic and operational value of using drones to transport vaccines. Vaccine. 2016;34(34):4062–7.
  27. Sachan D. The age of drones: what might it mean for health? Lancet. 2016;387(10030):1803–4.
  28. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington DC: The National Academies Press; 2016. @theNASEM.
  29. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
  30. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis. 2015;115(3):211–52.
  31. The roadmap for health measurement and accountability 2015. Available from:

Most read articles by the same author(s)

1 2 > >>