Spread of Pathogens on Healthcare Institutions Networks: a modeling study – SPHINx

The proposed methodological approach combines mathematical modeling, Big Data analysis, epidemiology and health economics and is divided into 5 work-packages (WP):
WP1: Modeling pathogen spread within hospitals
We will develop an agent-based model of pathogen transmission that allows simulating any contact network between individuals within a hospital. The model will be parameterized and validated using the detailed 6-month longitudinal dataset provided by the MOSAR European study.
WP2: Modeling pathogen spread between hospitals and healthcare institutions
The French patient transfer network will be re-constructed and analyzed, using the national PMSI database. In a second stage, we will simulate the spread of pathogens along this network.
WP3: Modeling hospital-community interactions
Systematic analysis of the PMSI database will allow dividing the French territory into areas connected to specific hospitals. A patch diffusion model calibrated for different pathogens will then be developed.
WP4: Exploiting the models to propose control strategies
Eventually, the different scales will be integrated in a single framework. We will use this model to better understand and quantify the joint role played by multi-scale phenomena on the global diffusion of HAI in hospitals and assess control strategies, while taking into account model uncertainty.
WP5: Assessing the epidemic vulnerability of healthcare systems at different scales
We will use a novel analytical framework to compute the epidemic threshold, i.e. the degree of vulnerability of a population to disease epidemics, while accounting for the time variation of its contact patterns. This approach will allow synthesizing the results obtained through numerical simulations in WP1-3, help identify the mechanisms responsible for the resulting vulnerability and provide an alternative means to assess the control strategies of WP4.

PROJECT UNDERWAY

PROJECT UNDERWAY

* Duval A., Smith D., Guillemot D., Opatowski L., Temime L. (2019) CTCmodeler: An Agent-Based Framework to Simulate Pathogen Transmission Along an Inter-individual Contact Network in a Hospital. In: Rodrigues J. et al. (eds) Computational Science – ICCS 2019. Lecture Notes in Computer Science, vol 11537. Springer, Cham
* Duval A, Obadia T, Boëlle PY, Fleury E, Herrmann JL, Guillemot D, Temime L, Opatowski L, i-Bird Study group. Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings. PLoS Comput Biol. 2019 May 30;15(5):e1006496
* Darbon A, Colombi D, Valdano E, Savini L, Giovannini A, Colizza V. Disease persistence on temporal contact networks accounting for heterogeneous infectious periods. bioRxiv 2018.
* Duval A, Obadia T, Martinet L, Boëlle PY, Fleury E, Guillemot D, Opatowski L, Temime L ; I-Bird study group.Measuring dynamic social contacts in a rehabilitation hospital : effect of wards, patient and staff characteristics. Scientific Reports. 2018 Jan 26 ;8(1):1686.
* Nekkab N, Astagneau P, Temime L, Crépey P. Spread of Hospital-Acquired Infections : A Comparison of Healthcare Networks. PLoS Comput Biol. 2017 Aug ;13(8):e1005666.
* Assab R, Nekkab N, Crépey P, Astagneau P, Guillemot D, Opatowski L, Temime L. Mathematical models of infection transmission in healthcare settings : recent advances from the use of network structured data. Current Opinion in Infectious Diseases. 2017 Aug ;30(4):410-418.

Despite advances in modern biology and medicine, healthcare-associated infections (HAI) have been occurring with increased incidence and severity over the last decades, becoming a major public health issue. A wide range of control strategies is already available, including hygiene measures, barrier precautions, antimicrobial stewardship, vaccination, patient isolation, cohorting, etc., based on screening programmes and surveillance systems. However, faced with the continued spread of HAI, the implementation of these strategies needs to be optimized and new control strategies must be considered and evaluated. Mathematical modelling and computer simulations are powerful tools which can help public-health practitioners examine possible courses of dissemination of HAI and assess the efficacy of control strategies.
The successful diffusion of a pathogen in a healthcare system results from a combination of processes operating at different scales. The way human populations are structured within and outside health-care institutions and the networks of patient transfers between wards and between institutions are layers that intrinsically interact with the biological layer of pathogen transmission and with the microbiological population dynamics layer of within-host pathogen selection. The processes operating at those different scales may have opposite effects on HAI dynamics which make HAI spread prediction and control strategies assessment difficult when considering only one scale. Yet, previously published modelling studies were typically limited to describing one of these scales, separately from the others. This separation challenges our ability to get a global understanding of HAI spread.
In this context, the objective of the SPHINx project is to propose a global approach to better understand and control the spread of HAI integrating various scales (hospital wards, hospitals and healthcare facilities networks, community) by developing a multi-scale computational framework.
First, we will build one model for each scale to understand how HAI can emerge, be selected and spread within and between healthcare facilities. Then, we will integrate these models in a multi-scale model to reproduce potential interferences between the different scales. The models will be informed using both published data and original data including data on patient transfers between healthcare facilities and the community at the French national level, as well as a detailed dataset on inter-individual contacts, microbial carriage status of patients and healthcare workers and antibiotic exposure in a single hospital over a 4-month period. Finally, we will use the developed multi-scale framework to assess and compare the potential impact of various control strategies on HAI spread and incidence, and assess their cost effectiveness from healthcare facility, payer and societal perspectives.
By increasing our knowledge of the healthcare-associated infection dynamics at various scales, the outputs of this project should contribute to design efficient and cost-effective integrated infection control measures at an individual, local, regional and national level.