Modelling the impact of relaxing COVID ‐19 control measures during a period of low viral transmission

Abstract

Objectives To assess the risks associated with relaxing coronavirus disease 2019 ( COVID ‐19)‐related physical distancing restrictions and lockdown policies during a period of low viral transmission. Design Network‐based viral transmission risks in households, schools, workplaces, and a variety of community spaces and activities were simulated in an agent‐based model, Covasim. Setting The model was calibrated for a baseline scenario reflecting the epidemiological and policy environment in Victoria during March–May 2020, a period of low community viral transmission. Intervention Policy changes for easing COVID ‐19‐related restrictions from May 2020 were simulated in the context of interventions that included testing, contact tracing (including with a smartphone app), and quarantine. Main outcome measure Increase in detected COVID ‐19 cases following relaxation of restrictions. Results Policy changes that facilitate contact of individuals with large numbers of unknown people (eg, opening bars, increased public transport use) were associated with the greatest risk of COVID ‐19 case numbers increasing; changes leading to smaller, structured gatherings with known contacts (eg, small social gatherings, opening schools) were associated with lower risks. In our model, the rise in case numbers following some policy changes was notable only two months after their implementation. Conclusions Removing several COVID ‐19‐related restrictions within a short period of time should be undertaken with care, as the consequences may not be apparent for more than two months. Our findings support continuation of work from home policies (to reduce public transport use) and strategies that mitigate the risk associated with re‐opening of social venues.

Identifying Synergistic Interventions to Address COVID-19 Using a Large Scale Agent-Based Model

“…This transparency has been emphasized early on [6] and cautionary tales showed its necessity, as high-profile models which were often cited by government agencies turned out to contain bugs and problematic assumptions once the code was later released and inspected [32]. Second, the toolkit has been used in several peer-reviewed studies [48, 57], which provides an additional layer of scrutiny and higher quality expectations than unpublished e-prints (arXiv, medRxiv) or ‘preprints’.…”

The role of mathematical models in developing policies for controlling COVID‐19 transmission

“…The Burnet Institute Covasim model takes a different approach 10 . An agent‐based model, it attempts to simulate interactions between individuals to estimate outcomes at the population level.…”

“…However, they can be helpful in defining the relative risk associated with various scenarios. For example, the Covasim model suggests that opening pubs and bars early would be risky, but this risk can be mitigated by high uptake of a contact tracing app, or by physical distancing 10 …”

“…The Burnet Institute Covasim model takes a different approach. 10 An agent-based model, it attempts to simulate interactions between individuals to estimate outcomes at the population level. Unlike compartmental models, they require the estimation of many parameters, including the number of contacts and the likelihood of transmission in different settings.…”

“…For example, the Covasim model suggests that opening pubs and bars early would be risky, but this risk can be mitigated by high uptake of a contact tracing app, or by physical distancing. 10 It should be emphasised that models are only one tool used for formulating policy. Many factors cannot be easily captured by models.…”