This is a preprint version of an article submitted for publication in the Medical Journal of Australia. Changes may be made before final publication. Click here for the PDF version. Suggested citation: Scott N, Palmer A, Delport D, Abeysuriya R, Stuart R, Kerr CC, Mistry D, Klein D, Sacks-Davis R, Heath K, Hainsworth S, Pedrana A, Stoove M, Wilson D, Hellard ME. Modelling the impact of reducing control measures on the COVID-19 pandemic in a low transmission setting. Med J Aust 2020; https://www.mja.com.au/journal/2020/modelling-impact-reducing-control-measures-covid-19-pandemic-low-transmission-setting [Preprint, 2 September 2020].
Objectives: We assessed coronavirus disease 2019 (COVID-19) epidemic risks associated with relaxing a set of physical distancing restrictions.
Design: An agent-based model, Covasim, was used to simulate network-based transmission risks in households, schools, workplaces, and a variety of community spaces (e.g. public transport, parks, bars, cafes/restaurants) and activities (e.g. community or professional sports, large events).
Setting: The model was calibrated to the COVID-19 epidemiological and policy environment in Victoria, Australia, between March and May 2020, at a time when there was low community transmission.
Participants: Model-simulated Victorian population.
Intervention: From May 2020, policy changes to ease restrictions were simulated (e.g. opening/closing businesses) in the context of interventions that included testing, contact tracing (including via a smartphone app), and quarantine.
Main outcome measure: Simulated epidemic rebound following relaxation of restrictions.
Results: Policy changes leading to the gathering of large, unstructured groups with unknown individuals (e.g. bars opening, increased public transport use) posed the greatest risk of epidemic rebound, while policy changes leading to smaller, structured gatherings with known individuals (e.g. small social gatherings) posed least risk of epidemic rebound. In the model, epidemic rebound following some policy changes took more than two months to occur. Model outcomes support continuation of working from home policies to reduce public transport use, and risk mitigation strategies in the context of social venues opening.
Conclusions: Care should be taken to avoid lifting sequential COVID-19 policy restrictions within short time periods, as it could take more than two months to detect the consequences of any changes.
Keywords: agent-based model, COVID-19, COVIDSAFE Australia, smartphone contact tracing app, networks, policy change, physical distancing
The Australian government released a framework for relaxing COVID-19 restrictions, however the risks associated with relaxing individual physical distancing policies are unknown.
Using an agent-based model, we found that it could take >2 months to detect epidemic rebound from a policy change. Large gatherings of unknown contacts pose the highest risk, while small gatherings of known contacts pose the least risk.
Sequential COVID-19 restrictions should not be lifted within short periods. Working from home should continue, to minimise public transport use. Additional physical distancing policies are required to mitigate the risks of opening pubs/bars.
Following a rise in cases of coronavirus disease 2019 (COVID-19), in March 2020 the Australian government introduced mandatory quarantine periods for people returning from overseas, as well as a variety of physical distancing policies, including closing pubs, bars, entertainment venues, churches/places of worship, restricting restaurants and cafes to take-away only, and limiting public gatherings to two people . Two months after these policies were introduced, available epidemic data indicate that they were successful in disrupting the spread of COVID-19, with fewer than 55 cases per day diagnosed nationally between 12 April and 8 May, down from a peak of 469 diagnosed cases on 28 March [2, 3]. On 8 May, the federal government released a framework (“COVIDSAFE Australia” ) that outlined a sequence of policy options to reopen different sectors, allowing states and territories to adopt different timings. Public health measures were also implemented including a scale-up of testing capacity and the release of the contact tracing smartphone app “COVIDSafe”.
Victoria is Australia’s second most populous state with an estimated population of 6.65 million (~26% of the nation’s total) . Until the end of May, the Victorian epidemic followed a similar trajectory to Australia as a whole, with an increase in daily new diagnoses throughout March to a peak of 111 on 29 March followed by a rapid decline as various restrictions were imposed. At 15 May (the time this analysis was conducted) there were 1,554 confirmed COVID-19 cases, the vast majority of which were among quarantined returned travellers . Due to minimal community transmission, Victoria relaxed restrictions to allow small social gatherings (13 May), cafes/restaurants to open with physical distancing policies (1 June) and community sports to recommence (22 June). Subsequently, in late June/early July Victoria experienced a resurgence in infections; 12,674 cases were detected between 14 June and 9 August (a cumulative 14,824 at 9 August) and various restrictions were re-imposed, leading to a second epidemic wave peak of 695 newly detected cases on the 5th August . The Victorian example illustrates that for countries entering COVID-19 response phases that involve relaxing restrictions, the sequence and timing of relaxing policies must be carefully considered so as not to compromise the overall effectiveness of the response. Epidemic modelling can provide insight into the likely impact of relaxing individual control measures.
Epidemic models can be broadly classified as either population-level or individual-level. Population-level models divide a population into a small number of discrete risk categories and assume homogeneous mixing and transmission risks within each category. In contrast, agent-based models use a set of autonomous ‘agents’ to represent a population and offer a more complex method for simulating individual-level characteristics and human behaviour . In reality, the risk of COVID-19 transmission is highly heterogeneous and driven by the contact networks of individuals, which are dependent on age, household structure and participation in different social and community activities. The impact of interventions to slow the spread of COVID-19, such as contact tracing and quarantine measures, are highly contact network dependent and are captured most effectively in individual-level models.
To our knowledge, no modelling is currently available for Australia that provides scenario analyses of the impact of “micro-policy” changes being proposed in the COVIDSAFE Australia framework. Population-level models [7-10] have been used to support the initial roll-out of physical distancing policies in Australia, and agent-based models are increasingly being used to simulate the impact of social distancing measures on COVID-19 transmission in Australia and internationally [11-18]; however these models are currently only considering the implementation of contact tracing, quarantine or social distancing policies rather than their release.
In this study we used an agent-based model, Covasim , to assess the risks associated with relaxing various physical distancing and lockdown policies in Victoria, Australia, from a low transmission epidemic state as occurred between March and May 2020.
The Covasim model is described in detail elsewhere  and reports are available outlining its application to a number of other settings . In brief, each person in the model is characterised by a set of demographic, disease and intervention status variables. Demographics variables include: age (one-year brackets); uniquely identified household, school (for people aged 5-18) and work (for people aged 18-65) contacts; and average number of daily contacts in a collection of community networks and settings (described in Appendix A - available in PDF). Disease variables include: infection status (susceptible, exposed, recovered or dead); viral load (time-varying); age-specific susceptibility; and age-specific probabilities of being symptomatic, experiencing different disease severities (mild, severe, critical), and mortality. Person-level intervention status variables include: diagnostic status (untested, tested and waiting for results, tested and received results) and quarantine status (yes/no).
Transmission is modelled to occur when a susceptible individual is in contact with an infectious individual through one of their contact networks. The per-day probability of transmission per contact with an infected person (“transmissibility”) is calibrated to match the epidemic dynamics observed, and is weighted according to whether the infectious individual has symptoms, and the type/setting of the contact (e.g. transmission is more likely with household contacts than community contacts).
Model details are in Appendix A; the model’s age-mixing and network structure are shown in Appendix B (Figures S1-S4) - available in PDF; disease parameters are in Appendix C (Tables S1-S2) - available in PDF; behavioural and network parameters are in Appendix D (Tables S3-S8, Figure S9) - available in PDF; and policy changes that can be made in the model are in Appendix E
Baseline scenario and calibration
A baseline scenario was run between 1 March and 30 April, which included the Victorian policy changes that had occurred over that period (Appendix F, Figure S10) - available in PDF. The overall probability of transmission per contact was calibrated such that the model projections fit the diagnosis and mortality data.
Scenario set 1: Policy relaxations
Multiple scenarios were run with different restrictions lifted in isolation starting from 15 May (the date of analysis): opening pubs/bars; allowing large events; opening cafes and restaurants; allowing community sports; allowing small social gatherings; opening entertainment venues (e.g. cinemas, performing arts); removing work from home directives (resulting in greater public transport use as well as more work interactions); and opening schools. The parameter and network configuration changes associated with relaxing each restriction are described in Appendix D - available in PDF. For each scenario, a number of new infections were introduced for modelling purposes (a theoretical five infections on 15 May) to restart the epidemic and test the robustness of the new policy configuration to outbreaks.
Scenario set 2: Contact tracing smartphone app
We estimated the threshold population-level coverage that a contact tracing smartphone app (i.e. COVIDSafe) would need to mitigate the risks of relaxing different policies. The threshold target was calculated to mitigate the risks associated with the policies of opening of pubs/bars and removing work from home directions, as these were the policies found to have the greatest risk (see results). Multiple scenarios were run where these policies were changed but with population-level coverage of the contact tracing app ranging from 0-50%.
Scenario set 3: Physical distancing policies within venues
Policy options are being utilized by governments to mitigate the risks associated with opening of cafés, restaurants, pubs and bars; for example, transmissibility in these settings could be reduced by implementing the "4 square metre rule", limits on customer numbers, or restricting venues to outside service only. We estimate how effective these additional interventions would need to be to mitigate the risks associated with opening these venues. Opening pubs/bars was used as an example as it was found to pose the greatest risk, and multiple scenarios were run where transmissibility within pubs and bars was reduced by 0-50%.
Scenario set 4: Patron records at venues
An additional policy option being used is for venues (pubs/bars/cafes/restaurants) to keep mandatory identification records of patrons, which would enable contact tracing following a diagnosed case. We estimate the threshold compliance with this policy required to mitigate the risks associated with opening these venues. Multiple scenarios were run where pubs/bars were opened but with the capacity to contact trace 40-80% of contacts following a within-venue transmission event.
A reasonable model fit was obtained (Figure 1 - available in PDF) that included the initial increase in cases observed followed by the subsequent decline in cases following the introduction of specific policy changes.
Scenario set 1: Policy relaxations
The greatest risk of a rebound in cases comes from policy changes that facilitate random, once-off mixing in the community, or situations where individuals have a large number of contacts, particularly those that are unknown. This includes opening pubs and bars (without additional restrictions), removing work from home directives (which increases public transport and work interactions) or allowing large events (concerts, sporting crowds, protest marches). The least risk comes from policy changes that facilitate smaller numbers of contacts, or repeated contacts with the same people (e.g. small social gatherings of under 10 people) (Figure 2 - available in PDF).
Importantly, for some policy changes the time before new infections begin to rapidly increase could be greater than two months (Figure 2, for example cafes/restaurants or entertainment venues opening - available in PDF).
Scenario set 2: Contact tracing smartphone app
Greater than 30% coverage was required before the app showed significant impact on mitigating population-level transmission risks (Figure 3 for pubs and bars being opened, and Figure S5 for working from home directives being removed - available in PDF).
Scenario sets 3-4: Mitigation strategies in venues
Opening pubs and bars (without additional restrictions) was found to be the policy that led to the greatest increase in new infections. However, the model suggests that if physical distancing policies within these settings could reduce transmissibility by more than 40% they could considerably mitigate the risks of them opening (Figure 4; also Figure S7 in combination with the smartphone app - available in PDF). Alternatively, recording the identification of patrons attending pubs and bars to enable effective contact tracing would be an effective policy at a population-level if compliance was greater than 60% (Figure S6 - available in PDF).
Using an agent-based model we have simulated the relaxation of a variety of policy restrictions in a low transmission setting in Australia. We found that policy changes that facilitate increasing numbers of contacts between people who are unknown to each other (e.g. pubs and bars opening, increased public transport use through removal of work from home directives, or large events) posed the greatest risk, while policy changes leading to smaller numbers of contacts within networks of known individuals (e.g. small social gatherings of under 10 people) posed the least risk. Importantly, the model suggests that it could take more than two months to detect increases in new infections from a change in policy, and therefore governments should avoid easing multiple restrictions within short time periods. These outcomes have implications for other settings with low community transmission where governments are lifting restrictions following relatively successful early responses.
Despite social and economic pressures to fast-track a return to normal conditions, our results suggest that restraint is needed, even in low transmission settings, because a resurgence in the epidemic following some policy changes could take more than two months to establish and be detected. In the model, contact tracing is effective for known contacts (Table S7 - available in PDF); however, transmission to unknown community contacts can still occur. For some policy configurations it is the chains of transmission through unknown contacts that may represent a minority of new cases initially, but if allowed to continue provide an increasing cumulative risk for epidemic expansion. It is therefore essential that testing services are readily accessible and provide rapid turnaround of results to complement contract tracing programs to ensure the timely detection of community transmission from unknown sources. This is vital to interrupt ongoing transmission networks.
The greatest risks of a resurgence in cases were associated with policy changes that allowed individuals to have large contact networks (e.g. crowded public transport, crowded pubs/bars, sports events) that introduce once-off mixing between unknown individuals in the community. In particular, these findings support the Victorian government’s decision to extend work from home directions for people who are able until at least July 2020, to minimise use of public transport . Further modelling could assess whether staggered work starting times (to limit crowding) or increased ventilation and cleaning could mitigate the risks associated with increased public transport use.
The lowest risks were associated with policy changes that led to smaller numbers of contacts for individuals, introduced organized contact network structure (e.g. known contacts), or introduced easily traceable contacts (e.g. family or small social gatherings of less than 10 people). Under these network configurations, population-wide connectivity remains restricted, limiting the potential for wide-scale population spread. In addition, known contacts have a greater probability of being traced in a timely way when transmission does occur. However, even for networks of known contacts, the risk of a resurgence in cases increases with increasing network size.
We found that a contact tracing smartphone app (i.e. COVIDSafe) would need greater than 30% effective population coverage to mitigate the risks associated with most policy relaxations. The effectiveness of the app relies on both the infected and susceptible person having a compatible phone, downloading the app and using it correctly. If 30% of the population correctly use the app, this would produce an additional 9% (30%*30%) of contacts able to be reliably traced. As of end May, approximately 6 million Australians had downloaded the COVIDSafe app (~24% of the population), meaning that the app could trace at most an additional ~6% (24%*24%) of contacts. Therefore, while the app could be effective at high coverage, it is likely to have minimal impact for low-moderate coverage.
Based on the current epidemiological situation, we estimated that to mitigate the risks of opening pubs and bars (the policy change found to pose the greatest risk), physical distancing strategies that can reduce COVID-19 transmissibility by at least 40% in these settings are required. The model cannot identify what interventions may be able to achieve this, but this provides a useful target for designing interventions that consist of a mix of hygiene measures, physical distancing and limits to patron numbers. The model also identified that venues keeping mandatory identification records of patrons could be an effective policy if it enabled greater that 60% of contacts to be traced (Figure S5 - available in PDF). Note that for mandatory identification to be as effective as the smartphone app, it needs to be more stringent, since the app has additional benefits by tracing multiple generations of transmissions rather than only those in the source setting.
In our projections, opening schools was one of the lower risk policies. This was predominantly because school contacts were known, making the contact tracing intervention effective in this environment, and because school contacts (e.g. classrooms) did not change over time for the duration of these simulations, creating a clustering of infections rather than population spread in the event of an outbreak. In the model, people aged under 20 years were also assumed to be less susceptible to infection than people over 20 years (people aged 0-9 or 10-19 have relative susceptibility of 0.34 or 0.67 respectively, Table S2 - available in PDF); however a sensitivity analysis where susceptibility was equal across ages (Figure S8 - available in PDF) showed robustness to this parameter. Another influencing factor is that the probability of people under 20 years being symptomatic in the model is lower than for people over 20 years, based on best available evidence (Table S2 - available in PDF), with asymptomatic cases having reduced transmissibility in the model.
Limitations and further work
The main limitations to this work are around model features, disease epidemiology parameters and contact network parameters.
This model currently only attributes basic properties to individuals, specifically age, household structure and participation in different contact networks. Therefore, the model does not account for any other demographic and health characteristics such as socioeconomic status, comorbidities (e.g. non-communicable diseases) and risk factors (e.g. smoking) and so cannot account for differences in transmission risks, testing, quarantine adherence or disease outcomes for different population subgroups. Further work is required with the specific aims of assessing the impact of policy changes on different subsets of the community, as well as geographical clustering.
Data reported on disease parameters such as duration of asymptomatic and infectious periods, as well as age-specific estimates of susceptibility, transmissibility and disease severity and are likely to be influenced by differences in surveillance systems in the countries they are being reported from. We have taken the best available data at the time, but this is likely to change as new information becomes available, and the model should be updated accordingly.
Contact networks are the most important factor driving COVID-19 transmission yet limited studies are available that provide the parameters needed to model them. The modified Delphi process used has potential biases in the non-randomly selected panel, and the large variation in parameter estimates suggests a high degree of uncertainty in contact network parameters. Despite this uncertainty, we argue that it is still important to consider these contact networks and the impact of policy changes on them. For example, studies are not available to quantify the relative transmissibility among public transport contacts compared to household contacts. However, omitting this parametrisation would implicitly either ignore public transport contacts, or assume that they are equal to household contacts. In this study we have instead assumed that they fall somewhere in between, but we do not know where and hence have used a panel to estimate. Similarly, if people are instructed to work from home, then the transmission risk on public transport would be expected to decrease. While the actual reduction is unclear, if this feature were not included then this would implicitly assume that there was no change. It is critical that these parameters are continually updated as new evidence becomes available.
In settings with low community transmission, care should be taken to avoid introducing multiple policy changes within short time periods, as it could take greater that two months to detect the consequences of any changes. Governments should be particularly wary of lifting restrictions that facilitate a larger number of contacts between people who do not know each other; instead favouring relaxing restrictions to allow smaller gatherings with known contacts.
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