Modelling the impact of reopening schools in early 2021 in the presence of the new SARS-CoV-2 variant and with roll-out of vaccination against COVID-19 (preprint)

Background Following the resurgence of the COVID-19 epidemic in the UK in late 2020 and the emergence of the new variant of the SARS-CoV-2 virus, B.1.1.7, a third national lockdown was imposed from January 5, 2021. Following the decline of COVID-19 cases over the remainder of January 2021, it is important to assess the conditions under which reopening schools from early March is likely to lead to resurgence of the epidemic. This study models the impact of a partial national lockdown with social distancing measures enacted in communities and workplaces under different strategies of reopening schools from March 8, 2021 and compares it to the impact of continual full national lockdown remaining until April 19, 2021. Methods We used our previously published model, Covasim, to model the emergence of B.1.1.7 over September 1, 2020 to January 31, 2021. We extended the model to incorporate the impacts of the roll-out of a two-dose vaccine against COVID-19, assuming 200,000 daily doses of the vaccine in people 75 years or older with vaccination that offers 95% reduction in disease acquisition and 10% reduction of transmission blocking. We used the model, calibrated until January 25, 2021, to simulate the impact of a full national lockdown (FNL) with schools closed until April 19, 2021 versus four different partial national lockdown (PNL) scenarios with different elements of schooling open: 1) staggered PNL with primary schools and exam-entry years (years 11 and 13) returning on March 8, 2021 and the rest of the schools years on March 15, 2020; 2) full-return PNL with both primary and secondary schools returning on March 8, 2021; 3) primary-only PNL with primary schools and exam critical years (Y11 and Y13) going back only on March 8, 2021 with the rest of the secondary schools back on April 19, 2021 and 4) part-Rota PNL with both primary and secondary schools returning on March 8, 2021 with primary schools remaining open continuously but secondary schools on a two-weekly rota-system with years alternating between a fortnight of face-to-face and remote learning until April 19, 2021. Across all scenarios, we projected the number of new daily cases, cumulative deaths and effective reproduction number R until April 30, 2020. Results Our calibration across different scenarios is consistent with the new variant B.1.1.7 being around 60% more transmissible. Strict social distancing measures, i.e. national lockdowns, are required to contain the spread of the virus and control the hospitalisations and deaths during January and February 2021. The national lockdown will reduce the number of cases by early March levels similar to those seen in October with R also falling and remaining below 1 during the lockdown. Infections start to increase when schools open but if other parts of society remain closed this resurgence is not sufficient to bring R above 1. Reopening primary schools and exam critical years only or having primary schools open continuously with secondary schools on rotas will lead to lower increases in cases and R than if all schools open. Under the current vaccination assumptions and across the set of scenarios considered, R would increase above 1 if society reopens simultaneously, simulated here from April 19, 2021.Findings Our findings suggest that stringent measures are necessary to mitigate the increase in cases and bring R below 1 over January and February 2021. It is plausible that a PNL with schools partially open from March 8, 2021 and the rest of the society remaining closed until April 19, 2021 may keep R below 1, with some increase evident in infections compared to continual FNL until April 19, 2021. Reopening society in mid-April, with the vaccination strategy we model, could push R above 1 and induce a surge in infections, but the effect of vaccination may be able to control this in future depending on the transmission blocking properties of the vaccines.

Controlling COVID-19 via test-trace-quarantine (preprint)

Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We performed this analysis using Covasim, an open-source agent-based model, which was calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we found that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.

Preventing a cluster from becoming a new wave in settings with zero community COVID-19 cases (preprint)

In settings with zero community transmission, any new SARS-CoV-2 outbreaks are likely to be the result of random incursions. The level of restrictions in place at the time of the incursion is likely to considerably affect possible outbreak trajectories. We used an agent-based model to investigate the relationship between ongoing restrictions and behavioural factors, and the probability of an incursion causing an outbreak and the resulting growth rate. We applied our model to the state of Victoria, Australia, which has reached zero community transmission as of November 2020. We found that a future incursion has a 45% probability of causing an outbreak (defined as a 7-day average of >5 new cases per day within 60 days) if no restrictions were in place, decreasing to 23% with a mandatory masks policy, density restrictions on venues such as restaurants, and if employees worked from home where possible. A drop in community symptomatic testing rates was associated with up to a 10-percentage point increase in outbreak probability, highlighting the importance of maintaining high testing rates as part of a suppression strategy. Because the chance of an incursion occurring is closely related to border controls, outbreak risk management strategies require an integrated approaching spanning border controls, ongoing restrictions, and plans for response. Each individual restriction or control strategy reduces the risk of an outbreak. They can be traded off against each other, but if too many are removed there is a danger of accumulating an unsafe level of risk. The outbreak probabilities estimated in this study are of particular relevance in assessing the downstream risks associated with increased international travel.

Lessons learned from Vietnam’s COVID-19 response: the role of adaptive behavior change and testing in epidemic control (preprint)

Background Vietnam has emerged as one of the world’s leading success stories in responding to COVID-19. After prolonged zero-low transmission, a summer outbreak of unknown source at Da Nang caused the country’s first COVID-19 deaths, but was quickly suppressed. Vietnam recently reopened its borders to international travelers. Understanding the attendant risks and how to minimize them is crucial as Vietnam moves into this new phase. Methods We create an agent-based model of COVID-19 in Vietnam, using regional testing data and a detailed linelist of the 1,014 COVID-19 cases, including 35 deaths, identified across Vietnam. We investigate the Da Nang outbreak, and quantify the risk of another outbreak under different assumptions about behavioral/policy responses and ongoing testing. Results The Da Nang outbreak, although rapidly contained once detected, nevertheless caused significant community transmission before it was detected; higher symptomatic testing could have mitigated this. If testing levels do not increase, the adoption of past policies in response to newly-detected cases may reduce the size of potential outbreaks but will not prevent them. Compared to a baseline symptomatic testing rate of 10%, we estimate half as many infections under a 20% testing rate, and a quarter as many with 40-50% testing rates, over the four months following border reopenings. Conclusions Vietnam’s success in controlling COVID-19 is largely attributable to its rapid response to detected outbreaks, but the speed of response could be improved even further with higher levels of symptomatic testing.

Modelling the impact of reducing control measures on the COVID-19 pandemic in a low transmission setting (preprint)

Aims: We assessed COVID-19 epidemic risks associated with relaxing a set of physical distancing restrictions in the state of Victoria, Australia - a setting with low community transmission - in line with a national framework that aims to balance sequential policy relaxations with longer-term public health and economic need. Methods: An agent-based model, Covasim, was calibrated to the local COVID-19 epidemiological and policy environment. Contact networks were modelled to capture transmission risks in households, schools and 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). Policy changes that could prevent or reduce transmission in specific locations (e.g. opening/closing businesses) were modelled in the context of interventions that included testing, contact tracing (including via a smartphone app), and quarantine. 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, while policy changes leading to smaller, structured gatherings with known individuals (e.g. small social gatherings) posed least risk. In the model, epidemic impact 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, such as >30% population-uptake of a contact-tracing app, physical distancing policies within venues reducing transmissibility by >40%, or patron identification records being kept to enable >60% contact tracing. Conclusions: In a low transmission setting, 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. These findings have implications for other settings with low community transmission where governments are beginning to lift restrictions.