ABSTRACT
Participatory agent-based modelling (ABM) can help bring the benefits of simulation to domain users by actively involving stakeholders in the development process. Collaboration in enterprise modelling can improve the model developer's understanding of the domain and therefore improve the effectiveness of domain analysis. Where many agent-oriented methodologies focus on the development of one-off models, domain-specific modelling languages (DSML) can improve the re-use of concepts identified in domain analysis across multiple case studies and expose modelling concepts in domain-appropriate terms, increasing model accessibility. To realise the benefits of DSMLs we need to understand how DSML development can be incorporated into typical agent-based modelling. In this paper we discuss existing methodologies for ABM development and DSML development, and we discuss the benefits merging the two can bring. We present a methodology for DSML-assisted participatory agent-based modelling, and support the methodology with a case study—a modelling exercise conducted in collaboration with a hospital emergency department on the topic of infection control for COVID-19 and Influenza. © 2022, IFIP International Federation for Information Processing.
ABSTRACT
The COVID-19 pandemic has catalyzed the pace and scope of digital technology (DIT) use in healthcare. It has facilitated health promotion, disease prevention, diagnosis and treatment of secondary health concerns, patient engagement, monitoring treatment adherence, and surveillance. COVID-19-related large databases and medical and public health research have been shared freely and rapidly. Continued adoption of technology can lead to better and faster diagnosis of health conditions and accelerate the attainment of sustainable development goals (SDGs). However, many barriers still remain. Key issues are high costs, lack of interoperability of technology, frequent need for software updates, training and development, concerns about privacy, technological disruption, and network coverage issues. Greater involvement of end users in the development and rollout of new digital technologies is needed to ensure faster and deeper implementation of technology in healthcare as uneven implementation can exacerbate the divide between the haves and the have-nots. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
BACKGROUND: Point-of-care (POC) SARS-CoV-2 lateral-flow antigen detection (LFD) testing in the emergency department (ED) could inform rapid infection control decisions but requirements for safe deployment have not been fully defined. METHODS: Review of LFD test results, laboratory and POC-RT-PCR results and ED-performance metrics during a two-week high SARS-CoV-2 prevalence period followed by several months of falling prevalence. AIM: Determine whether LFD testing can be safely deployed in ED to provide an effective universal SARS-CoV-2 testing capability. FINDINGS: 93% (345/371) of COVID-19 patients left ED with a virological diagnosis during the 2-week universal LFD evaluation period compared to 77% with targeted POC-RT-PCR deployment alone, on background of approximately one-third having an NHS Track and Trace RT-PCR test-result at presentation. LFD sensitivity and specificity was 70.7% and 99.1% respectively providing a PPV of 97.7% and NPV of 86.4% with disease prevalence of 34.7%. ED discharge-delays (breaches) attributable to COVID-19 fell to 33/3532 (0.94%) compared with the preceding POC-RT-PCR period (107/4114 (2.6%); p=<0.0001). Importantly, LFD testing identified 1 or 2 clinically-unsuspected COVID-19 patients/day. Three clinically-confirmed LFD false positive patients were appropriately triaged based on LFD action-card flowchart, and only 5 of 95 false-negative LFD results were inappropriately admitted to non-COVID-19 areas where no onward-transmission was identified. LFD testing was restricted to asymptomatic patients when disease prevalence fell below 5% and detected 1-3 cases/week. CONCLUSION: Universal SARS-CoV-2 LFD testing can be safely and effectively deployed in ED alongside POC-RT-PCR testing during periods of high and low disease prevalence.