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.

Covasim: An agent-based model of COVID-19 dynamics and interventions.

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

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.

Ending malnutrition in all its forms requires scaling up proven nutrition interventions and much more: a 129-country analysis.

BACKGROUND: Sustainable Development Goal (SDG) 2.2 calls for an end to all forms of malnutrition, with 2025 targets of a 40% reduction in stunting (relative to 2012), for wasting to occur in less than 5% of children, and for a 50% reduction in anaemia in women (15-49 years). We assessed the likelihood of countries reaching these targets by scaling up proven interventions and identified priority interventions, based on cost-effectiveness. METHODS: For 129 countries, the Optima Nutrition model was used to compare 2019-2030 nutrition outcomes between a status quo (maintained intervention coverage) scenario and a scenario where outcome-specific interventions were scaled up to 95% coverage over 5 years. The average cost-effectiveness of each intervention was calculated as it was added to an expanding package of interventions. RESULTS: Of the 129 countries modelled, 46 (36%), 66 (51%) and 0 (0%) were on track to achieve the stunting, wasting and anaemia targets respectively. Scaling up 18 nutrition interventions increased the number of countries reaching the SDG 2.2 targets to 50 (39%), 83 (64%) and 7 (5%) respectively. Intermittent preventative treatment of malaria during pregnancy (IPTp), infant and young child feeding education, vitamin A supplementation and lipid-based nutrition supplements for children produced 88% of the total impact on stunting, with average costs per case averted of US$103, US$267, US$556 and US$1795 when interventions were consecutively scaled up, respectively. Vitamin A supplementation and cash transfers produced 100% of the total global impact on prevention of wasting, with average costs per case averted of US$1989 and US$19,427, respectively. IPTp, iron and folic acid supplementation for non-pregnant women, and multiple micronutrient supplementation for pregnant women produced 85% of the total impact on anaemia prevalence, with average costs per case averted of US$9, US$35 and US$47, respectively. CONCLUSIONS: Prioritising nutrition investment to the most cost-effective interventions within the country context can maximise the impact of funding. A greater focus on complementing nutrition-specific interventions with nutrition-sensitive ones that address the social determinants of health is critical to reach the SDG targets.

COVID-19 outbreaks in residential aged care facilities: an agent-based modeling study.

Introduction: A disproportionate number of COVID-19 deaths occur in Residential Aged Care Facilities (RACFs), where better evidence is needed to target COVID-19 interventions to prevent mortality. This study used an agent-based model to assess the role of community prevalence, vaccination strategies, and non-pharmaceutical interventions (NPIs) on COVID-19 outcomes in RACFs in Victoria, Australia. Methods: The model simulated outbreaks in RACFs over time, and was calibrated to distributions for outbreak size, outbreak duration, and case fatality rate in Victorian RACFs over 2022. The number of incursions to RACFs per day were estimated to fit total deaths and diagnoses over time and community prevalence.Total infections, diagnoses, and deaths in RACFs were estimated over July 2023-June 2024 under scenarios of different: community epidemic wave assumptions (magnitude and frequency); RACF vaccination strategies (6-monthly, 12-monthly, no further vaccines); additional non-pharmaceutical interventions (10, 25, 50% efficacy); and reduction in incursions (30% or 60%). Results: Total RACF outcomes were proportional to cumulative community infections and incursion rates, suggesting potential for strategic visitation/staff policies or community-based interventions to reduce deaths. Recency of vaccination when epidemic waves occurred was critical; compared with 6-monthly boosters, 12-monthly boosters had approximately 1.2 times more deaths and no further boosters had approximately 1.6 times more deaths over July 2023-June 2024. Additional NPIs, even with only 10-25% efficacy, could lead to a 13-31% reduction in deaths in RACFs. Conclusion: Future community epidemic wave patterns are unknown but will be major drivers of outcomes in RACFs. Maintaining high coverage of recent vaccination, minimizing incursions, and increasing NPIs can have a major impact on cumulative infections and deaths.

A Framework for Assessing the Impact of Outbreak Response Immunization Programs.

The impact of outbreak response immunization (ORI) can be estimated by comparing observed outcomes to modelled counterfactual scenarios without ORI, but the most appropriate metrics depend on stakeholder needs and data availability. This study developed a framework for using mathematical models to assess the impact of ORI for vaccine-preventable diseases. Framework development involved (1) the assessment of impact metrics based on stakeholder interviews and literature reviews determining data availability and capacity to capture as model outcomes; (2) mapping investment in ORI elements to model parameters to define scenarios; (3) developing a system for engaging stakeholders and formulating model questions, performing analyses, and interpreting results; and (4) example applications for different settings and pathogens. The metrics identified as most useful were health impacts, economic impacts, and the risk of severe outbreaks. Scenario categories included investment in the response scale, response speed, and vaccine targeting. The framework defines four phases: (1) problem framing and data sourcing (identification of stakeholder needs, metrics, and scenarios); (2) model choice; (3) model implementation; and (4) interpretation and communication. The use of the framework is demonstrated by application to two outbreaks, measles in Papua New Guinea and Ebola in the Democratic Republic of the Congo. The framework is a systematic way to engage with stakeholders and ensure that an analysis is fit for purpose, makes the best use of available data, and uses suitable modelling methodology.

Keeping kids in school: modelling school-based testing and quarantine strategies during the COVID-19 pandemic in Australia.

Background: In 2021, the Australian Government Department of Health commissioned a consortium of modelling groups to generate evidence assisting the transition from a goal of no community COVID-19 transmission to 'living with COVID-19', with adverse health and social consequences limited by vaccination and other measures. Due to the extended school closures over 2020-21, maximizing face-to-face teaching was a major objective during this transition. The consortium was tasked with informing school surveillance and contact management strategies to minimize infections and support this goal. Methods: Outcomes considered were infections and days of face-to-face teaching lost in the 45 days following an outbreak within an otherwise COVID-naïve school setting. A stochastic agent-based model of COVID-19 transmission was used to evaluate a 'test-to-stay' strategy using daily rapid antigen tests (RATs) for close contacts of a case for 7 days compared with home quarantine; and an asymptomatic surveillance strategy involving twice-weekly screening of all students and/or teachers using RATs. Findings: Test-to-stay had similar effectiveness for reducing school infections as extended home quarantine, without the associated days of face-to-face teaching lost. Asymptomatic screening was beneficial in reducing both infections and days of face-to-face teaching lost and was most beneficial when community prevalence was high. Interpretation: Use of RATs in school settings for surveillance and contact management can help to maximize face-to-face teaching and minimize outbreaks. This evidence supported the implementation of surveillance testing in schools in several Australian jurisdictions from January 2022.

Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts.

Between June and August 2020, an agent-based model was used to project rates of COVID-19 infection incidence and cases diagnosed as positive from 15 September to 31 October 2020 for 72 geographic settings. Five scenarios were modelled: a baseline scenario where no future changes were made to existing restrictions, and four scenarios representing small or moderate changes in restrictions at two intervals. Post hoc, upper and lower bounds for number of diagnosed Covid-19 cases were compared with actual data collected during the prediction window. A regression analysis with 17 covariates was performed to determine correlates of accurate projections. It was found that the actual data fell within the lower and upper bounds in 27 settings and out of bounds in 45 settings. The only statistically significant predictor of actual data within the predicted bounds was correct assumptions about future policy changes (OR 15.04; 95% CI 2.20-208.70; p = 0.016). Frequent changes in restrictions implemented by governments, which the modelling team was not always able to predict, in part explains why the majority of model projections were inaccurate compared with actual outcomes and supports revision of projections when policies are changed as well as the importance of modelling teams collaborating with policy experts.

Health and economic benefits of achieving contraceptive and maternal health targets in Small Island Developing States in the Pacific and Caribbean.

INTRODUCTION: Reducing unmet need for modern contraception and expanding access to quality maternal health (MH) services are priorities for improving women's health and economic empowerment. To support investment decisions, we estimated the additional cost and expected health and economic benefits of achieving the United Nations targets of zero unmet need for modern contraceptive choices and 95% coverage of MH services by 2030 in select Small Island Developing States. METHODS: Five Pacific (Kiribati, Samoa, Solomon Islands, Tonga and Vanuatu) and four Caribbean (Barbados, Guyana, Jamaica and Saint Lucia) countries were considered based on population survey data availability. For each country, the Lives Saved Tool was used to model costs, health outcomes and economic benefits for two scenarios: business-as-usual (BAU) (coverage maintained) and coverage-targets-achieved, which scaled linearly from 2022 (following COVID-19 disruptions) coverage of evidence-based family planning and MH interventions to reach United Nations targets, including modern contraceptive methods and access to complete antenatal, delivery and emergency care. Unintended pregnancies, maternal deaths, stillbirths and newborn deaths averted by the coverage-targets-achieved scenario were converted to workforce, education and social economic benefits; and benefit-cost ratios were calculated. RESULTS: The coverage-targets-achieved scenario required an additional US$12.6M (US$10.8M-US$15.9M) over 2020-2030 for the five Pacific countries (15% more than US$82.4M to maintain BAU). This additional investment was estimated to avert 126 000 (40%) unintended pregnancies, 2200 (28%) stillbirths and 121 (29%) maternal deaths and lead to a 15-fold economic benefit of US$190.6M (US$67.0M-US$304.5M) by 2050. For the four Caribbean countries, an additional US$17.8M (US$15.3M-US$22.4M) was needed to reach the targets (4% more than US$405.4M to maintain BAU). This was estimated to avert 127 000 (23%) unintended pregnancies, 3600 (23%) stillbirths and 221 (25%) maternal deaths and lead to a 24-fold economic benefit of US$426.2M (US$138.6M-US$745.7M) by 2050. CONCLUSION: Achieving full coverage of contraceptive and MH services in the Pacific and Caribbean is likely to have a high return on investment.

Cost optimisation analysis of the expanded programme for immunisation: balancing equity and coverage in Pakistan.

INTRODUCTION: With limited resources, attaining maximal average health service coverage can be at odds with maximising equity which attempts to promote greater reach among underserved populations. In this study, we examined the trade-offs in immunisation coverage levels and equity for children under 5 years of age in Pakistan across various subpopulations who can be targeted with different combinations of immunisation service modalities. METHODS: We conducted a detailed costing exercise across 16 geographically and demographically diverse districts in Pakistan. These data were the basis for (a) technical efficiency benchmarking via Data Envelopment Analysis to identify potential efficiency gains by location, delivery model and cost ingredient; (b) allocative efficiency optimisation modelling to understand how resource allocations could be optimised and to devise recommended budget allocations and operational metrics. Finally, the hypothetical overall efficiency gains attainable were estimated if available resources were allocated with the optimal emphases, and if service delivery models operated at productivity levels at the benchmarked frontier of efficiency. RESULTS: Benchmarking suggests that ~44% of delivery models are running efficiently and 37% are highly inefficient. While coverage and equity are usually at odds, surprisingly, the optimisation modelling revealed that substantial improvements in equity between subpopulations does not necessarily cost very much in overall immunisation coverage: theoretically, equity can be achieved while still attaining close to maximal immunisation coverage. Overall, analyses suggest greater emphases should be placed on outreach delivery models which particularly target rural areas and slum populations. CONCLUSION: The unit cost differentials within districts are not sufficiently large for there to be a large reduction in potential Fully Immunised Children coverage if one focuses on maximising equity. However, reallocations of programme budgets can have a significant impact on equity outcomes, particularly at current low spending amounts. Therefore, it is recommended to address equity as the key objective in national immunisation programming.