Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data.

Résumé : PURPOSE OF REVIEW: Mathematical modeling approaches have brought important contributions to the study of pathogen spread in healthcare settings over the last 20 years. Here, we conduct a comprehensive systematic review of mathematical models of disease transmission in healthcare settings and assess the application of contact and patient transfer network data over time and their impact on our understanding of transmission dynamics of infections. RECENT FINDINGS: Recently, with the increasing availability of data on the structure of interindividual and interinstitution networks, models incorporating this type of information have been proposed, with the aim of providing more realistic predictions of disease transmission in healthcare settings. Models incorporating realistic data on individual or facility networks often remain limited to a few settings and a few pathogens (mostly methicillin-resistant Staphylococcus aureus). SUMMARY: To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.
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Current Opinion in Infectious Diseases, Lippincott, Williams & Wilkins, 2017, 30 (4), pp.410-418. 〈10.1097/QCO.0000000000000390〉
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Rania Assab, Narimane Nekkab, Pascal Crepey, Pascal Astagneau, Didier Guillemot, et al.. Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data.. Current Opinion in Infectious Diseases, Lippincott, Williams & Wilkins, 2017, 30 (4), pp.410-418. 〈10.1097/QCO.0000000000000390〉. 〈hal-01548909〉

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