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Mathematical Tools for Understanding Infectious Disease Dynamics$
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Odo Diekmann, Hans Heesterbeek, and Tom Britton

Print publication date: 2012

Print ISBN-13: 9780691155395

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691155395.001.0001

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Data-driven modeling of hospital infections

Data-driven modeling of hospital infections

(p.325) Chapter Fourteen Data-driven modeling of hospital infections
Mathematical Tools for Understanding Infectious Disease Dynamics

Odo Diekmann

Hans Heesterbeek

Tom Britton

Princeton University Press

The treatment of hospital patients suffering from bacterial infections is increasingly hampered by antibiotic resistance. When the means of curing infections diminish, the prevention of infection gains importance. If we want to ascertain the effectiveness of various potential control measures, we need to know whether cross-transmission or opportunistic growth is the predominant colonization route. For the former, advocating hand washing and other traditional methods of infection prevention makes sense, whereas in the latter case a more prudent use of the arsenal of antibiotics should be pursued. But how to assess the relative importance of these two routes to colonization? This chapter shows in detail how to model the dynamics of a pathogen in a very small dynamic population (nosocomial infections in an Intensive Care Unit of a hospital) based on the type of data that will be routinely available. It shows that maximum likelihood estimation is feasible when bookkeeping is done in an efficient way. An alternative approach would be to iteratively improve guesses about missing data in a Bayesian setting, using the Markov chain Monte Carlo method.

Keywords:   infectious disease, maximum likelihood estimation, Markov chain Monte Carlo method, hospital infections, disease control, disease prevention, bacterial infections, hospital patients

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