BLDE University Journal of Health Sciences

REVIEW ARTICLE
Year
: 2018  |  Volume : 3  |  Issue : 2  |  Page : 69--74

Health analytics and disease modeling for better understanding of healthcare-associated infections


Martin Lopez-Garcia1, Meghana Aruru2, Saumyadipta Pyne3 
1 Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK
2 Department of Pharmacy and Therapeutics, Program Evaluation and Research Unit, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
3 PHDL, Department of Biostatistics, Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA; ICMR National Institute of Medical Statistics, New Delhi, India

Correspondence Address:
Dr. Meghana Aruru
Department of Pharmacy and Therapeutics, Program Evaluation and Research Unit, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA
USA

Healthcare-associated infections (HAIs) are a growing challenge and a major cause of health concern worldwide. It is difficult to understand precisely the dynamics of spread of hospital-acquired infections owing to the usual involvement of different populations, risk factors, environments, and pathogens. Mathematical and computational models have proved to be useful tools in providing realistic representations of HAI dynamics and the means of evaluating interventions to minimize the risk of HAIs.


How to cite this article:
Lopez-Garcia M, Aruru M, Pyne S. Health analytics and disease modeling for better understanding of healthcare-associated infections.BLDE Univ J Health Sci 2018;3:69-74


How to cite this URL:
Lopez-Garcia M, Aruru M, Pyne S. Health analytics and disease modeling for better understanding of healthcare-associated infections. BLDE Univ J Health Sci [serial online] 2018 [cited 2019 Aug 20 ];3:69-74
Available from: http://www.bldeujournalhs.in/article.asp?issn=2468-838X;year=2018;volume=3;issue=2;spage=69;epage=74;aulast=Lopez-Garcia;type=0