Epidemiologic and economic modelling of optimal COVID-19 policy: public health and social measures, masks and vaccines in Victoria, Australia.

Background: Identifying optimal COVID-19 policies is challenging. For Victoria, Australia (6.6 million people), we evaluated 104 policy packages (two levels of stringency of public health and social measures [PHSMs], by two levels each of mask-wearing and respirator provision during large outbreaks, by 13 vaccination schedules) for nine future SARS-CoV-2 variant scenarios. Methods: We used an agent-based model to estimate morbidity, mortality, and costs over 12 months from October 2022 for each scenario. The 104 policies (each averaged over the nine future variant scenarios) were ranked based on four evenly weighted criteria: cost-effectiveness from (a) health system only and (b) health system plus GDP perspectives, (c) deaths and (d) days exceeding hospital occupancy thresholds. Findings: More compared to less stringent PHSMs reduced cumulative infections, hospitalisations and deaths but also increased time in stage ≥3 PHSMs. Any further vaccination from October 2022 decreased hospitalisations and deaths by 12% and 27% respectively compared to no further vaccination and was usually a cost-saving intervention from a health expenditure plus GDP perspective. High versus low vaccine coverage decreased deaths by 15% and reduced time in stage ≥3 PHSMs by 20%. The modelled mask policies had modest impacts on morbidity, mortality, and health system pressure. The highest-ranking policy combination was more stringent PHSMs, two further vaccine doses (an Omicron-targeted vaccine followed by a multivalent vaccine) for ≥30-year-olds with high uptake, and promotion of increased mask wearing (but not Government provision of respirators). Interpretation: Ongoing vaccination and PHSMs continue to be key components of the COVID-19 pandemic response. Integrated epidemiologic and economic modelling, as exemplified in this paper, can be rapidly updated and used in pandemic decision making. Funding: Anonymous donation, University of Melbourne funding.

The health impact of long COVID during the 2021-2022 Omicron wave in Australia: a quantitative burden of disease study.

BACKGROUND: Long COVID symptoms occur for a proportion of acute COVID-19 survivors, with reduced risk among the vaccinated and for Omicron compared with Delta variant infections. The health loss attributed to pre-Omicron long COVID has previously been estimated using only a few major symptoms. METHODS: The years lived with disability (YLDs) due to long COVID in Australia during the 2021-22 Omicron BA.1/BA.2 wave were calculated using inputs from previously published case-control, cross-sectional or cohort studies examining the prevalence and duration of individual long COVID symptoms. This estimated health loss was compared with acute SARS-CoV-2 infection YLDs and years of life lost (YLLs) from SARS-CoV-2. The sum of these three components equals COVID-19 disability-adjusted life years (DALYs); this was compared with DALYs from other diseases. RESULTS: A total of 5200 [95% uncertainty interval (UI) 2200-8300] YLDs were attributable to long COVID and 1800 (95% UI 1100-2600) to acute SARS-CoV-2 infection, suggesting long COVID caused 74% of the overall YLDs from SARS-CoV-2 infections in the BA.1/BA.2 wave. Total DALYs attributable to SARS-CoV-2 were 50 900 (95% UI 21 000-80 900), 2.4% of expected DALYs for all diseases in the same period. CONCLUSION: This study provides a comprehensive approach to estimating the morbidity due to long COVID. Improved data on long COVID symptoms will improve the accuracy of these estimates. As data accumulate on SARS-CoV-2 infection sequelae (e.g. increased cardiovascular disease rates), total health loss is likely to be higher than estimated in this study. Nevertheless, this study demonstrates that long COVID requires consideration in pandemic policy planning, given it is responsible for the majority of direct SARS-CoV-2 morbidity, including during an Omicron wave in a highly vaccinated population.

Housing and health: an updated glossary.

Recent crises have underscored the importance that housing has in sustaining good health and, equally, its potential to harm health. Considering this and building on Howden-Chapman's early glossary of housing and health and the WHO Housing and Health Guidelines, this paper introduces a range of housing and health-related terms, reflecting almost 20 years of development in the field. It defines key concepts currently used in research, policy and practice to describe housing in relation to health and health inequalities. Definitions are organised by three overarching aspects of housing: affordability (including housing affordability stress (HAS) and fuel poverty), suitability (including condition, accessibility and sustainable housing) and security (including precarious housing and homelessness). Each of these inter-related aspects of housing can be either protective of, or detrimental to, health. This glossary broadens our understanding of the relationship between housing and health to further promote interdisciplinarity and strengthen the nexus between these fields.

Association of Simulated COVID-19 Policy Responses for Social Restrictions and Lockdowns With Health-Adjusted Life-Years and Costs in Victoria, Australia.

Importance: Countries have varied enormously in how they have responded to the COVID-19 pandemic, ranging from elimination strategies (eg, Australia, New Zealand, Taiwan) to tight suppression (not aiming for elimination but rather to keep infection rates low [eg, South Korea]) to loose suppression (eg, Europe, United States) to virtually unmitigated (eg, Brazil, India). Weighing the best option, based on health and economic consequences due to lockdowns, is necessary. Objective: To determine the optimal policy response, using a net monetary benefit (NMB) approach, for policies ranging from aggressive elimination and moderate elimination to tight suppression (aiming for 1-5 cases per million per day) and loose suppression (5-25 cases per million per day). Design Setting and Participants: Using governmental data from the state of Victoria, Australia, and other collected data, 2 simulation models in series were conducted of all residents (population, 6.4 million) for SARS-CoV-2 infections for 1 year from September 1, 2020. An agent-based model (ABM) was used to estimate daily SARS-CoV-2 infection rates and time in 5 stages of social restrictions (stages 1, 1b, 2, 3, and 4) for 4 policy response settings (aggressive elimination, moderate elimination, tight suppression, and loose suppression), and a proportional multistate life table (PMSLT) model was used to estimate health-adjusted life-years (HALYs) associated with COVID-19 and costs (health systems and health system plus gross domestic product [GDP]). The ABM is a generic COVID-19 model of 2500 agents, or simulants, that was scaled up to the population of interest. Models were specified with data from 2019 (eg, epidemiological data in the PMSLT model) and 2020 (eg, epidemiological and cost consequences of COVID-19). The NMB of each policy option at varying willingness to pay (WTP) per HALY was calculated: NMB = HALYs × WTP - cost. The estimated most cost-effective (optimal) policy response was that with the highest NMB. Main Outcome and Measures: Estimated SARS-CoV-2 infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses. Results: In 100 runs of both the ABM and PMSLT models for each of the 4 policy responses, 31.0% of SARS-CoV-2 infections, 56.5% of hospitalizations, and 84.6% of deaths occurred among those 60 years and older. Aggressive elimination was associated with the highest percentage of days with the lowest level of restrictions (median, 31.7%; 90% simulation interval [SI], 6.6%-64.4%). However, days in hard lockdown were similar across all 4 strategies. The HALY losses (compared with a scenario without COVID-19) were similar for aggressive elimination (median, 286 HALYs; 90% SI, 219-389 HALYs) and moderate elimination (median, 314 HALYs; 90% SI, 228-413 HALYs), and nearly 8 and 40 times higher for tight suppression and loose suppression, respectively. The median GDP loss was least for moderate elimination (median, $41.7 billion; 90% SI, $29.0-$63.6 billion), but there was substantial overlap in simulation intervals between the 4 strategies. From a health system perspective, aggressive elimination was optimal in 64% of simulations above a WTP of $15 000 per HALY, followed by moderate elimination in 35% of simulations. Moderate elimination was optimal from a GDP perspective in half of the simulations, followed by aggressive elimination in a quarter. Conclusions and Relevance: In this simulation modeling economic evaluation of estimated SARS-CoV-infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses in Victoria, Australia, an elimination strategy was associated with the least health losses and usually the fewest GDP losses.

A log-odds system for waning and boosting of COVID-19 vaccine effectiveness.

Immunity to SARS-CoV-2 following vaccination wanes over time in a non-linear fashion, making modelling of likely population impacts of COVID-19 policy options challenging. We observed that it was possible to mathematize non-linear waning of vaccine effectiveness (VE) on the percentage scale as linear waning on the log-odds scale, and developed a random effects logistic regression equation based on UK Health Security Agency data to model VE against Omicron following two and three doses of a COVID-19 vaccine. VE on the odds scale reduced by 47% per month for symptomatic infection after two vaccine doses, lessening to 35% per month for hospitalisation. Waning on the odds scale after triple dose vaccines was 35% per month for symptomatic disease and 19% for hospitalisation. This log-odds system for estimating waning and boosting of COVID-19 VE provides a simple solution that may be used to parametrize SARS-CoV-2 immunity over time parsimoniously in epidemiological models.

Modelling SARS-CoV-2 disease progression in Australia and New Zealand: an account of an agent-based approach to support public health decision-making.

OBJECTIVE: In 2020, we developed a public health decision-support model for mitigating the spread of SARS-CoV-2 infections in Australia and New Zealand. Having demonstrated its capacity to describe disease progression patterns during both countries' first waves of infections, we describe its utilisation in Victoria in underpinning the State Government's then 'RoadMap to Reopening'. METHODS: Key aspects of population demographics, disease, spatial and behavioural dynamics, as well as the mechanism, timing, and effect of non-pharmaceutical public health policies responses on the transmission of SARS-CoV-2 in both countries were represented in an agent-based model. We considered scenarios related to the imposition and removal of non-pharmaceutical interventions on the estimated progression of SARS-CoV-2 infections. RESULTS: Wave 1 results suggested elimination of community transmission of SARS-CoV-2 was possible in both countries given sustained public adherence to social restrictions beyond 60 days' duration. However, under scenarios of decaying adherence to restrictions, a second wave of infections (Wave 2) was predicted in Australia. In Victoria's second wave, we estimated in early September 2020 that a rolling 14-day average of <5 new cases per day was achievable on or around 26 October. Victoria recorded a 14-day rolling average of 4.6 cases per day on 25 October. CONCLUSIONS: Elimination of SARS-CoV-2 transmission represented in faithfully constructed agent-based models can be replicated in the real world. IMPLICATIONS FOR PUBLIC HEALTH: Agent-based public health policy models can be helpful to support decision-making in novel and complex unfolding public health crises.

Association of Simulated COVID-19 Policy Responses for Social Restrictions and Lockdowns With Health-Adjusted Life-Years and Costs in Victoria, Australia

This economic evaluation determines the optimal policy response to the COVID-19 pandemic in Victoria, Australia, using a net monetary benefit approach for policies ranging from aggressive elimination and moderate elimination to tight suppression and loose suppression. Key Points Question What has the least health losses and is the most cost-effective of 4 policy responses to the COVID-19 pandemic (aggressive elimination, moderate elimination, tight suppression, and loose suppression) in the state of Victoria, Australia? Findings In this simulation modeling economic evaluation of health losses and costs from COVID-19 policy responses, aggressive elimination was the most cost-effective from a health system perspective in 64% of simulations above a willingness to pay of $15 000 per health-adjusted life-years, followed by moderate elimination in 35% of simulations. Moderate elimination was most cost-effective from a gross domestic product (GDP) perspective (ie, including GDP losses in addition to health expenditure) in half of the simulations, followed by aggressive elimination in a quarter. Meaning While there is considerable uncertainty in outcomes for all 4 policy responses, the 2 elimination options appear to be the most optimal from both health system and health plus GDP perspectives. Importance Countries have varied enormously in how they have responded to the COVID-19 pandemic, ranging from elimination strategies (eg, Australia, New Zealand, Taiwan) to tight suppression (not aiming for elimination but rather to keep infection rates low [eg, South Korea]) to loose suppression (eg, Europe, United States) to virtually unmitigated (eg, Brazil, India). Weighing the best option, based on health and economic consequences due to lockdowns, is necessary. Objective To determine the optimal policy response, using a net monetary benefit (NMB) approach, for policies ranging from aggressive elimination and moderate elimination to tight suppression (aiming for 1-5 cases per million per day) and loose suppression (5-25 cases per million per day). Design, Setting, and Participants Using governmental data from the state of Victoria, Australia, and other collected data, 2 simulation models in series were conducted of all residents (population, 6.4 million) for SARS-CoV-2 infections for 1 year from September 1, 2020. An agent-based model (ABM) was used to estimate daily SARS-CoV-2 infection rates and time in 5 stages of social restrictions (stages 1, 1b, 2, 3, and 4) for 4 policy response settings (aggressive elimination, moderate elimination, tight suppression, and loose suppression), and a proportional multistate life table (PMSLT) model was used to estimate health-adjusted life-years (HALYs) associated with COVID-19 and costs (health systems and health system plus gross domestic product [GDP]). The ABM is a generic COVID-19 model of 2500 agents, or simulants, that was scaled up to the population of interest. Models were specified with data from 2019 (eg, epidemiological data in the PMSLT model) and 2020 (eg, epidemiological and cost consequences of COVID-19). The NMB of each policy option at varying willingness to pay (WTP) per HALY was calculated: NMB = HALYs × WTP − cost. The estimated most cost-effective (optimal) policy response was that with the highest NMB. Main Outcome and Measures Estimated SARS-CoV-2 infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses. Results In 100 runs of both the ABM and PMSLT models for each of the 4 policy responses, 31.0% of SARS-CoV-2 infections, 56.5% of hospitalizations, and 84.6% of deaths occurred among those 60 years and older. Aggressive elimination was associated with the highest percentage of days with the lowest level of restrictions (median, 31.7%;90% simulation interval [SI], 6.6%-64.4%). However, days in hard lockdown were similar across all 4 strategies. The HALY losses (compared with a scenario without COVID-19) were similar for aggressive eliminatio (median, 286 HALYs;90% SI, 219-389 HALYs) and moderate elimination (median, 314 HALYs;90% SI, 228-413 HALYs), and nearly 8 and 40 times higher for tight suppression and loose suppression, respectively. The median GDP loss was least for moderate elimination (median, $41.7 billion;90% SI, $29.0-$63.6 billion), but there was substantial overlap in simulation intervals between the 4 strategies. From a health system perspective, aggressive elimination was optimal in 64% of simulations above a WTP of $15 000 per HALY, followed by moderate elimination in 35% of simulations. Moderate elimination was optimal from a GDP perspective in half of the simulations, followed by aggressive elimination in a quarter. Conclusions and Relevance In this simulation modeling economic evaluation of estimated SARS-CoV-infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses in Victoria, Australia, an elimination strategy was associated with the least health losses and usually the fewest GDP losses.

Failures of quarantine systems for preventing COVID-19 outbreaks in Australia and New Zealand.

OBJECTIVES: To identify COVID-19 quarantine system failures in Australia and New Zealand. DESIGN, SETTING, PARTICIPANTS: Observational epidemiological study of travellers in managed quarantine in Australia and New Zealand, to 15 June 2021. MAIN OUTCOME MEASURES: Number of quarantine system failures, and failure with respect to numbers of travellers and SARS-CoV-2-positive travellers. RESULTS: We identified 22 quarantine system failures in Australia and ten in New Zealand to 15 June 2021. One failure initiated a COVID-19 outbreak that caused more than 800 deaths (the Victorian "second wave"); nine lockdowns were linked with quarantine system failures. The failure risk was estimated to be 5.0 failures per 100 000 travellers passing through quarantine and 6.1 (95% CI, 4.0-8.3) failures per 1000 SARS-CoV-2-positive travellers. The risk per 1000 SARS-CoV-2-positive travellers was higher in New Zealand than Australia (relative risk, 2.0; 95% CI, 1.0-4.2). CONCLUSIONS: Quarantine system failures can be costly in terms of lives and economic impact, including lockdowns. Our findings indicate that infection control in quarantine systems in Australia and New Zealand should be improved, including vaccination of quarantine workers and incoming travellers, or that alternatives to hotel-based quarantine should be developed.

Estimating the impact of control measures to prevent outbreaks of COVID-19 associated with air travel into a COVID-19-free country.

We aimed to estimate the risk of COVID-19 outbreaks associated with air travel to a COVID-19-free country [New Zealand (NZ)]. A stochastic version of the SEIR model CovidSIM v1.1, designed specifically for COVID-19 was utilised. We first considered historical data for Australia before it eliminated COVID-19 (equivalent to an outbreak generating 74 new cases/day) and one flight per day to NZ with no interventions in place. This gave a median time to an outbreak of 0.2 years (95% range of simulation results: 3 days to 1.1 years) or a mean of 110 flights per outbreak. However, the combined use of a pre-flight PCR test of saliva, three subsequent PCR tests (on days 1, 3 and 12 in NZ), and various other interventions (mask use and contact tracing) reduced this risk to one outbreak after a median of 1.5 years (20 days to 8.1 years). A pre-flight test plus 14 days quarantine was an even more effective strategy (4.9 years; 2,594 flights). For a much lower prevalence (representing only two new community cases per week in the whole of Australia), the annual risk of an outbreak with no interventions was 1.2% and had a median time to an outbreak of 56 years. In contrast the risks associated with travellers from Japan and the United States was very much higher and would need quarantine or other restrictions. Collectively, these results suggest that multi-layered interventions can markedly reduce the risk of importing the pandemic virus via air travel into a COVID-19-free nation. For some low-risk source countries, there is the potential to replace 14-day quarantine with alternative interventions. However, all approaches require public and policy deliberation about acceptable risks, and continuous careful management and evaluation.

Estimating the risk of outbreaks of COVID-19 associated with shore leave by merchant ship crews: simulation studies for New Zealand.

AIM: We aimed to estimate the risk of COVID-19 outbreaks in a COVID-19-free destination country (New Zealand) associated with shore leave by merchant ship crews who were infected prior to their departure or on their ship. METHODS: We used a stochastic version of the SEIR model CovidSIM v1.1 designed specifically for COVID-19. It was populated with parameters for SARS-CoV-2 transmission, shipping characteristics and plausible control measures. RESULTS: When no control interventions were in place, we estimated that an outbreak of COVID-19 in New Zealand would occur after a median time of 23 days (assuming a global average for source country incidence of 2.66 new infections per 1,000 population per week, crews of 20 with a voyage length of 10 days and 1 day of shore leave per crew member both in New Zealand and abroad, and 108 port visits by international merchant ships per week). For this example, the uncertainty around when outbreaks occur is wide (an outbreak occurs with 95% probability between 1 and 124 days). The combination of PCR testing on arrival, self-reporting of symptoms with contact tracing and mask use during shore leave increased this median time to 1.0 year (14 days to 5.4 years, or a 49% probability within a year). Scenario analyses found that onboard infection chains could persist for well over 4 weeks, even with crews of only 5 members. CONCLUSION: This modelling work suggests that the introduction of SARS-CoV-2 through shore leave from international shipping crews is likely, even after long voyages. But the risk can be substantially mitigated by control measures such as PCR testing and mask use.