COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020-2021.

BACKGROUND: Policy responses to COVID-19 in Victoria, Australia over 2020-2021 have been supported by evidence generated through mathematical modelling. This study describes the design, key findings, and process for policy translation of a series of modelling studies conducted for the Victorian Department of Health COVID-19 response team during this period. METHODS: An agent-based model, Covasim, was used to simulate the impact of policy interventions on COVID-19 outbreaks and epidemic waves. The model was continually adapted to enable scenario analysis of settings or policies being considered at the time (e.g. elimination of community transmission versus disease control). Model scenarios were co-designed with government, to fill evidence gaps prior to key decisions. RESULTS: Understanding outbreak risk following incursions was critical to eliminating community COVID-19 transmission. Analyses showed risk depended on whether the first detected case was the index case, a primary contact of the index case, or a 'mystery case'. There were benefits of early lockdown on first case detection and gradual easing of restrictions to minimise resurgence risk from undetected cases. As vaccination coverage increased and the focus shifted to controlling rather than eliminating community transmission, understanding health system demand was critical. Analyses showed that vaccines alone could not protect health systems and need to be complemented with other public health measures. CONCLUSIONS: Model evidence offered the greatest value when decisions needed to be made pre-emptively, or for questions that could not be answered with empiric data and data analysis alone. Co-designing scenarios with policy-makers ensured relevance and increased policy translation.

Estimating the proportion of Victorians infected with COVID-19 during the Omicron BA.1 epidemic wave of January 2022 in Australia.

OBJECTIVE: To estimate the proportion of Victorians infected with COVID-19 in January 2022. METHODS: Between 11-19 February 2022 we conducted a nested cross-sectional survey on experiences of COVID-19 testing, symptoms, test outcome and barriers to testing during January 2022 in Victoria, Australia. Respondents were participants of the Optimise Study, a prospective cohort of adults considered at increased risk of COVID-19 or the unintended consequences of COVID-19-related interventions. RESULTS: Of the 577 participants, 78 (14%) reported testing positive to COVID-19, 240 (42%) did not test in January 2022 and 91 of those who did not test (38%) reported COVID-19-like symptoms. Using two different definitions of symptoms, we calculated symptomatic (27% and 39%) and asymptomatic (4% and 11%) test positivity. We extrapolated these positivity rates to participants who did not test and estimated 19-22% of respondents may have had COVID-19 infection in January 2022. CONCLUSION: The proportion of Victorians infected with COVID-19 in January 2022 was likely considerably higher than officially reported numbers. IMPLICATIONS FOR PUBLIC HEALTH: Our estimate is approximately double the COVID-19 case numbers obtained from official case reporting. This highlights a major limitation of diagnosis data that must be considered when preparing for future waves of infection.

Impact of COVID-19 lockdown restrictions on hepatitis C testing in Australian primary care services providing care for people who inject drugs.

In 2020, the Australian state of Victoria experienced the longest COVID-19 lockdowns of any jurisdiction, with two lockdowns starting in March and July, respectively. Lockdowns may impact progress towards eliminating hepatitis C through reductions in hepatitis C testing. To examine the impact of lockdowns on hepatitis C testing in Victoria, de-identified data were extracted from a network of 11 services that specialize in the care of people who inject drugs (PWID). Interrupted time-series analyses estimated weekly changes in hepatitis C antibody and RNA testing from 1 January 2019 to 14 May 2021 and described temporal changes in testing associated with lockdowns. Interruptions were defined at the weeks corresponding to the start of the first lockdown (week 14) and the start (week 80) and end (week 95) of the second lockdown. Pre-COVID, an average of 80.6 antibody and 25.7 RNA tests were performed each week. Following the first lockdown in Victoria, there was an immediate drop of 23.2 antibody tests and 8.6 RNA tests per week (equivalent to a 31% and 46% drop, respectively). Following the second lockdown, there was an immediate drop of 17.2 antibody tests and 4.6 RNA tests per week (equivalent to a 26% and 33% drop, respectively). With testing and case finding identified as a key challenge to Australia achieving hepatitis C elimination targets, the cumulative number of testing opportunities missed during lockdowns may prolong efforts to find, diagnose and engage or reengage in care of the remaining population of PWID living with hepatitis C.

Mathematical Modeling for Removing Border Entry and Quarantine Requirements for COVID-19, Vanuatu.

The Pacific Island country of Vanuatu is considering strategies to remove border restrictions implemented during 2020 to prevent imported coronavirus disease. We performed mathematical modeling to estimate the number of infectious travelers who had different entry scenarios and testing strategies. Travel bubbles and testing on entry have the greatest importation risk reduction.

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

BACKGROUND: 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, but the probability that a large outbreak eventuates is not known. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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

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.