Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus

Authored by Sen Pei, Flaviano Morone, Fredrik Liljeros, Hernan Makse, Jeffrey L Shaman

Date Published: 2018

DOI: 10.7554/elife.40977

Sponsors: United States National Institutes of Health (NIH) United States National Science Foundation (NSF)

Platforms: MATLAB

Model Documentation: Other Narrative Pseudocode

Model Code URLs: https://elifesciences.org/download/aHR0cHM6Ly9jZG4uZWxpZmVzY2llbmNlcy5vcmcvYXJ0aWNsZXMvNDA5NzcvZWxpZmUtNDA5NzctY29kZTEtdjEuemlw/elife-40977-code1-v1.zip?_hash=IiweMnTYQt07aKVmfAMaYihJnQcHUo7%2BLQZQZ9nkziY%3D

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) is a continued threat to human health in both community and healthcare settings. In hospitals, control efforts would benefit from accurate estimation of asymptomatic colonization and infection importation rates from the community. However, developing such estimates remains challenging due to limited observation of colonization and complicated transmission dynamics within hospitals and the community. Here, we develop an inference framework that can estimate these key quantities by combining statistical filtering techniques, an agent-based model, and real-world patient-to-patient contact networks, and use this framework to infer nosocomial transmission and infection importation over an outbreak spanning 6 years in 66 Swedish hospitals. In particular, we identify a small number of patients with disproportionately high risk of colonization. In retrospective control experiments, interventions targeted to these individuals yield a substantial improvement over heuristic strategies informed by number of contacts, length of stay and contact tracing.

Tags

Epidemiology Community MRSA Infections outbreak Surveillance United-states Risk-factors Health-care facilities Collective influence