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Yearb Med Inform 2015; 24(01): 216-219
DOI: 10.15265/IY-2015-031
Original Article
Georg Thieme Verlag KG Stuttgart

Modelling, Simulation and Social Network Data: What’s New for Public Health and Epidemiology Informatics?

L. Toubiana
1   INSERM UMRS 1142 LIMICS, Université Pierre et Marie Curie, Paris,France
,
N Griffon
1   INSERM UMRS 1142 LIMICS, Université Pierre et Marie Curie, Paris,France
2   BioMedical Informatics Department & CISMeF, TIBS, LITIS EA 4108, Rouen University Hospital, Normandy, Rouen, France
,
Section Editors for the IMIA Yearbook Section on Public Health and Epidemiology Informatics › Institutsangaben
Weitere Informationen

Correspondence to:

Dr. Nicolas Griffon
Unité d’Informatique Médicale
CHU de Rouen
1 rue de Germont
76031 Rouen, France
Telefon: +33 6 42 25 44 11   

Dr. Laurent Toubiana, PhD
INSERM UMRS 1142 “LIMICS”
15, rue de l’École de Médecine
75006 Paris, France
Telefon: +33 1 44 27 91 97   

Publikationsverlauf

13. August 2015

Publikationsdatum:
10. März 2018 (online)

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Summary

Objectives: Summarize excellent current research in the field of Public Health and Epidemiology Informatics.

Method: Synopsis of the articles selected for the IMIA Yearbook 2015.

Results: Four papers from international peer-reviewed journals have been selected as best papers for the section on Public Health and Epidemiology Informatics.

Conclusions: The selected articles illustrate current research regarding the impact and assessment of health IT and the latest developments in health information exchange.


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Keywords

Public health - epidemiology - medical informatics - International Medical Informatics Association - health information systems - hospital information systems

 


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  • References

  • 1 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015; 54 (02) 135-44.
    Thieme ConnectPubMedSuche in Google Scholar
  • 2 Dixon BE, Vreeman DJ, Grannis SJ. The long road to semantic interoperability in support of public health: experiences from two states. J Biomed Inform 2014; 49: 3-8.
    CrossrefPubMedSuche in Google Scholar
  • 3 Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R. Global disease monitoring and forecasting with Wikipedia. PLoS Comput Biol 2014; 10: e1003892.
    CrossrefPubMedSuche in Google Scholar
  • 4 Aslam AA, Tsou M-H, Spitzberg BH, An L, Gawron JM, Gupta DK. et al. The reliability of tweets as a supplementary method of seasonal influenza surveillance. J Med Internet Res 2014; 16: e250.
    CrossrefPubMedSuche in Google Scholar
  • 5 Yom-Tov E, Borsa D, Cox IJ, McKendry RA. Detecting disease outbreaks in mass gatherings using Internet data. J Med Internet Res 2014; 16: e154.
    CrossrefPubMedSuche in Google Scholar
  • 6 Barboza P, Vaillant L, Le Strat Y, Hartley DM, Nelson NP, Mawudeku A. et al. Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. PLoS One 2014; 9: e90536.
    CrossrefPubMedSuche in Google Scholar
  • 7 Pawelek KA, Oeldorf-Hirsch A, Rong L. Modeling the impact of twitter on influenza epidemics. Math Biosci Eng 2014; 11: 1337-56.
    CrossrefPubMedSuche in Google Scholar
  • 8 Khormi HM, Kumar L. Climate change and the potential global distribution of Aedes aegypti: spatial modelling using GIS and CLIMEX. Geospat Health 2014; 8: 405-15.
    CrossrefPubMedSuche in Google Scholar
  • 9 Nikakhtar A, Hsiang SM. Incorporating the dynamics of epidemics in simulation models of healthcare systems. Simul Model Pract Theory 2014; 43: 67-78.
    CrossrefPubMedSuche in Google Scholar
  • 10 Gupta A, Briat C, Khammash M. A scalable computational framework for establishing long-term behavior of stochastic reaction networks. PLoS Comput Biol 2014; 10: e1003669.
    CrossrefPubMedSuche in Google Scholar
  • 11 Jandarov R, Haran M, Bjørnstad O, Grenfell B. Emulating a gravity model to infer the spatiotemporal dynamics of an infectious disease. J R Stat Soc Ser C (Applied Stat 2014; 63: 423-44.
    CrossrefPubMedSuche in Google Scholar
  • 12 Rutter P, Mytton O, Ellis B, Donaldson L. Access to the NHS by telephone and Internet during an influenza pandemic: an observational study. BMJ Open 2014; 4: e004174.
    CrossrefPubMedSuche in Google Scholar
  • 13 Verdot C, Godin-Blandeau E, Grémy I, Develay A-E. Monitoring systems and national surveys on prison health in France and abroad. Eur J Public Health 2015; 25: 167-72.
    CrossrefPubMedSuche in Google Scholar

Correspondence to:

Dr. Nicolas Griffon
Unité d’Informatique Médicale
CHU de Rouen
1 rue de Germont
76031 Rouen, France
Telefon: +33 6 42 25 44 11   

Dr. Laurent Toubiana, PhD
INSERM UMRS 1142 “LIMICS”
15, rue de l’École de Médecine
75006 Paris, France
Telefon: +33 1 44 27 91 97   

  • References

  • 1 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015; 54 (02) 135-44.
    Thieme ConnectPubMedSuche in Google Scholar
  • 2 Dixon BE, Vreeman DJ, Grannis SJ. The long road to semantic interoperability in support of public health: experiences from two states. J Biomed Inform 2014; 49: 3-8.
    CrossrefPubMedSuche in Google Scholar
  • 3 Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R. Global disease monitoring and forecasting with Wikipedia. PLoS Comput Biol 2014; 10: e1003892.
    CrossrefPubMedSuche in Google Scholar
  • 4 Aslam AA, Tsou M-H, Spitzberg BH, An L, Gawron JM, Gupta DK. et al. The reliability of tweets as a supplementary method of seasonal influenza surveillance. J Med Internet Res 2014; 16: e250.
    CrossrefPubMedSuche in Google Scholar
  • 5 Yom-Tov E, Borsa D, Cox IJ, McKendry RA. Detecting disease outbreaks in mass gatherings using Internet data. J Med Internet Res 2014; 16: e154.
    CrossrefPubMedSuche in Google Scholar
  • 6 Barboza P, Vaillant L, Le Strat Y, Hartley DM, Nelson NP, Mawudeku A. et al. Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. PLoS One 2014; 9: e90536.
    CrossrefPubMedSuche in Google Scholar
  • 7 Pawelek KA, Oeldorf-Hirsch A, Rong L. Modeling the impact of twitter on influenza epidemics. Math Biosci Eng 2014; 11: 1337-56.
    CrossrefPubMedSuche in Google Scholar
  • 8 Khormi HM, Kumar L. Climate change and the potential global distribution of Aedes aegypti: spatial modelling using GIS and CLIMEX. Geospat Health 2014; 8: 405-15.
    CrossrefPubMedSuche in Google Scholar
  • 9 Nikakhtar A, Hsiang SM. Incorporating the dynamics of epidemics in simulation models of healthcare systems. Simul Model Pract Theory 2014; 43: 67-78.
    CrossrefPubMedSuche in Google Scholar
  • 10 Gupta A, Briat C, Khammash M. A scalable computational framework for establishing long-term behavior of stochastic reaction networks. PLoS Comput Biol 2014; 10: e1003669.
    CrossrefPubMedSuche in Google Scholar
  • 11 Jandarov R, Haran M, Bjørnstad O, Grenfell B. Emulating a gravity model to infer the spatiotemporal dynamics of an infectious disease. J R Stat Soc Ser C (Applied Stat 2014; 63: 423-44.
    CrossrefPubMedSuche in Google Scholar
  • 12 Rutter P, Mytton O, Ellis B, Donaldson L. Access to the NHS by telephone and Internet during an influenza pandemic: an observational study. BMJ Open 2014; 4: e004174.
    CrossrefPubMedSuche in Google Scholar
  • 13 Verdot C, Godin-Blandeau E, Grémy I, Develay A-E. Monitoring systems and national surveys on prison health in France and abroad. Eur J Public Health 2015; 25: 167-72.
    CrossrefPubMedSuche in Google Scholar

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