Hybrid simulation modelling of networks of heterogeneous care homes and the inter-facility spread of Covid-19 by sharing staff

Abstract

Although system dynamics [SD] and agent-based modelling [ABM] have individually served as effective tools to understand the Covid-19 dynamics, combining these methods in a hybrid simulation model can help address Covid-19 questions and study systems and settings that are difficult to study with a single approach. To examine the spread and outbreak of Covid-19 across multiple care homes via bank/agency staff and evaluate the effectiveness of interventions targeting this group, we develop an integrated hybrid simulation model combining the advantages of SD and ABM. We also demonstrate how we use several approaches adapted from both SD and ABM practices to build confidence in this model in response to the lack of systematic approaches to validate hybrid models. Our modelling results show that the risk of infection for residents in care homes using bank/agency staff was significantly higher than those not using bank/agency staff (Relative risk [RR] 2.65, 95% CI 2.57–2.72). Bank/agency staff working across several care homes had a higher risk of infection compared with permanent staff working in a single care home (RR 1.55, 95%CI 1.52–1.58). The RR of infection for residents is negatively correlated to bank/agency staff’s adherence to weekly PCR testing. Within a network of heterogeneous care homes, using bank/agency staff had the most impact on care homes with lower intra-facility transmission risks, higher staff-to-resident ratio, and smaller size. Forming bubbles of care homes had no or limited impact on the spread of Covid-19. This modelling study has implications for policy makers considering developing effective interventions targeting staff working across care homes during the ongoing and future pandemics.

The spread of coronavirus disease 2019 (COVID-19) via staff work and household networks in residential aged-care services in Victoria, Australia, May–October 2020

“…Although modeling studies have demonstrated that agency nurses are a key risk for the spread of SARS-CoV-2 across long-term care homes, this risk can be mitigated with better training, routine testing, and the formation of workplace "bubbles" that limit the number of care homes where staff work. 20,21 In addition to limiting worker mobility, initiatives that reduce the chance of onward transmission within the households of RACS staff members should be considered as part of the public health response to COVID-19 outbreaks in the aged-care sector. Other strategies could include readily accessible supported accommodation programs, which provide hotel or other accommodation for front-line staff who are working during an outbreak, 22 as well as broad close-contact definitions for RACS staff, provided there were adequate staff replacements available.…”

Health Systems Simulation Modelling to Support Decision-making in COVID-19 Prevention and Control in Care Homes

“…The objective of the integrated hybrid SD-ABM model was to understand the impact that temporary bank/agency staff, who work across multiple care homes, have on the spread of COVID-19 in care homes and to evaluate the effectiveness of a range of intervention strategies (Nguyen, Megiddo, and Howick, 2022).…”

Interfaces Between SD and ABM Modules in a Hybrid Model

“…A detailed discussion of categories of information flows follows. These categorizations emerged from a literature review of hybrid SD-ABM models across various domains and were based on reflection from the modeling process of our case study (Nguyen, Megiddo, and Howick 2022). For each category of information flow, we provide a description and one or two example models selected from the literature.…”

“…White and black boxes denote elements of an SD and ABM module, respectively. Green arrows denote the flows of information found in the literature, and red arrows denote the proposed flows based on reflection from the modeling process of our case study (Nguyen, Megiddo, and Howick 2022) (1) Stock levels affect agent-specific state variables or are used to generate a small crowd of agents; (2) Stock levels affect agent's behaviors; (3) Stock levels bound aggregate measures of agents; (4) Stock levels affect the network topology of agents; and (5), ( 6), (7), and (8): Agent-specific state variables, agents' behaviors, aggregated measures of agents, and the network topology of agents affect flows of an SD module respectively.…”