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

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


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
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/bjhs.bjhs_36_18

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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.


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