The changing impact of vaccines in the COVID-19 pandemic

The Omicron wave has left a global imprinting of immunity which changes the COVID landscape. In this study, we simulate six hypothetical variants emerging over the next year and evaluate the impact of existing and improved vaccines. We base our study on South Africas infection- and vaccination-derived immunity. Our findings illustrate that variant-chasing vaccines will only add value above existing vaccines in the setting where a variant emerges if we can shorten the window between variant introduction and vaccine deployment to under three weeks, an impossible time-frame without significant NPI use. This strategy may have global utility, depending on the rate of spread from setting to setting. Broadly neutralizing and durable next-generation vaccines could avert over three-times as many deaths from an immune-evading variant compared to existing vaccines. Our results suggest it is crucial to develop next-generation vaccines and redress inequities in vaccine distribution to tackle future emerging variants.

Predicting the unpredictable: how dynamic COVID-19 policies and restrictions challenge model forecasts

IntroductionTo retrospectively assess the accuracy of a mathematical modelling study that projected the rate of COVID-19 diagnoses for 72 locations worldwide in 2021, and to identify predictors of model accuracy. MethodsBetween 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. ResultsThe 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). ConclusionsFor this study, the accuracy of COVID-19 model projections was dependent on whether assumptions about future policies are correct. 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 policy experts collaborating on modelling projects.

Susceptibility of zero community transmission regimes to new variants of SARS-CoV-2: a modelling study of Queensland

We used an agent-based model Covasim to assess the risk of sustained community transmission of SARS-CoV-2/COVID-19 in Queensland (Australia) in the presence of high-transmission variants of the virus. The model was calibrated using the demographics, policies, and interventions implemented in the state. Then, using the calibrated model, we simulated possible epidemic trajectories that could eventuate due to leakage of infected cases with high-transmission variants, during a period without recorded cases of locally acquired infections, known in Australian settings as "zero community transmission". We also examined how the threat of new variants reduces given a range of vaccination levels. Specifically, the model calibration covered the first-wave period from early March 2020 to May 2020. Predicted epidemic trajectories were simulated from early February 2021 to late March 2021. Our simulations showed that one infected agent with the ancestral (A.2.2) variant has a 14% chance of crossing a threshold of sustained community transmission (SCT) (i.e., > 5 infections per day, more than 3 days in a row), assuming no change in the prevailing preventative and counteracting policies. However, one agent carrying the alpha (B.1.1.7) variant has a 43% chance of crossing the same threshold; a threefold increase with respect to the ancestral strain; while, one agent carrying the delta (B.1.617.2) variant has a 60% chance of the same threshold, a fourfold increase with respect to the ancestral strain. The delta variant is 50% more likely to trigger SCT than the alpha variant. Doubling the average number of daily tests from [~] 6,000 to 12,000 results in a decrease of this SCT probability from 43% to 33% for the alpha variant. However, if the delta variant is circulating we would need an average of 100,000 daily tests to achieve a similar decrease in SCT risk. Further, achieving a full-vaccination coverage of 70% of the adult population, with a vaccine with 70% effectiveness against infection, would decrease the probability of SCT from a single seed of alpha from 43% to 20%, on par with the ancestral strain in a naive population. In contrast, for the same vaccine coverage and same effectiveness, the probability of SCT from a single seed of delta would decrease from 62% to 48%, a risk slightly above the alpha variant in a naive population. Our results demonstrate that the introduction of even a small number of people infected with high-transmission variants dramatically increases the probability of sustained community transmission in Queensland. Until very high vaccine coverage is achieved, a swift implementation of policies and interventions, together with high quarantine adherence rates, will be required to minimise the probability of sustained community transmission.

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

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

BackgroundVietnam has emerged as one of the worlds leading success stories in responding to COVID-19. After prolonged zero-low transmission, a summer outbreak of unknown source at Da Nang caused the countrys 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. MethodsWe 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. ResultsThe 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. ConclusionsVietnams 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.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Robust test and trace strategies can prevent COVID-19 resurgences: a case study from New South Wales, Australia

ObjectivesThe early stages of the COVID-19 pandemic illustrated that SARS-CoV-2, the virus that causes the disease, has the potential to spread exponentially. Therefore, as long as a substantial proportion of the population remains susceptible to infection, the potential for new epidemic waves persists even in settings with low numbers of active COVID-19 infections, unless sufficient countermeasures are in place. We aim to quantify vulnerability to resurgences in COVID-19 transmission under variations in the levels of testing, tracing, and mask usage. SettingThe Australian state of New South Wales, a setting with prolonged low transmission, high mobility, non-universal mask usage, and a well-functioning test-and-trace system. ParticipantsNone (simulation study) ResultsWe find that the relative impact of masks is greatest when testing and tracing rates are lower (and vice versa). Scenarios with very high testing rates (90% of people with symptoms, plus 90% of people with a known history of contact with a confirmed case) were estimated to lead to a robustly controlled epidemic, with a median of [~]180 infections in total over October 1 - December 31 under high mask uptake scenarios, or 260-1,200 without masks, depending on the efficacy of community contact tracing. However, across comparable levels of mask uptake and contact tracing, the number of infections over this period were projected to be 2-3 times higher if the testing rate was 80% instead of 90%, 8-12 times higher if the testing rate was 65%, or 30-50 times higher with a 50% testing rate. In reality, NSW diagnosed 254 locally-acquired cases over this period, an outcome that had a low probability in the model (4-7%) under the best-case scenarios of extremely high testing (90%), near-perfect community contact tracing (75-100%), and high mask usage (50-75%), but a far higher probability if any of these were at lower levels. ConclusionsOur work suggests that testing, tracing and masks can all be effective means of controlling transmission. A multifaceted strategy that combines all three, alongside continued hygiene and distancing protocols, is likely to be the most robust means of controlling transmission of SARS-CoV-2. Strengths and limitations of this studyO_LIA key methodological strength of this study is the level of detail in the model that we use, which allows us to capture many of the finer details of the extent to which controlling COVID-19 transmission relies on the balance between testing, contact tracing, and mask usage. C_LIO_LIAnother key strength is that our model is stochastic, so we are able to quantify the probability of different epidemiological outcomes under different policy settings. C_LIO_LIA key limitation is the shortage of publicly-available data on the efficacy of contact tracing programs, including data on how many people were contacted for each confirmed index case of COVID-19. C_LI

The potential contribution of face coverings to the control of SARS-CoV-2 transmission in schools and broader society in the UK: a modelling study

Recent findings suggest that an adequate test-trace-isolate (TTI) strategy is needed to prevent a secondary COVID-19 wave with the reopening of society in the UK. Here we assess the potential importance of mandatory masks in the parts of community and in secondary schools. We show that, assuming current TTI levels, adoption of masks in secondary schools in addition to community settings can reduce the size of a second wave, but will not prevent it; more testing of symptomatic people, tracing and isolating of their contacts is also needed. To avoid a second wave, with masks mandatory in secondary schools and in certain community settings, under current tracing levels, 68% or 46% of those with symptomatic infection would need to be tested if masks effective coverage were 15% or 30% respectively, compared to 76% and 57% if masks are mandated in community settings but not secondary schools.

Schools are not islands: Balancing COVID-19 risk and educational benefits using structural and temporal countermeasures

BackgroundSchool closures around the world contributed to reducing the transmission of COVID-19. In the face of significant uncertainty around the epidemic impact of in-person schooling, policymakers, parents, and teachers are weighing the risks and benefits of returning to in-person education. In this context, we examined the impact of different school reopening scenarios on transmission within and outside of schools and on the share of school days that would need to be spent learning at a distance. MethodsWe used an agent-based mathematical model of COVID-19 transmission and interventions to quantify the impact of school reopening on disease transmission and the extent to which school-based interventions could mitigate epidemic spread within and outside schools. We compared seven school reopening strategies that vary the degree of countermeasures within schools to mitigate COVID-19 transmission, including the use of face masks, physical distancing, classroom cohorting, screening, testing, and contact tracing, as well as schedule changes to reduce the number of students in school. We considered three scenarios for the size of the epidemic in the two weeks prior to school reopening: 20, 50, or 110 detected cases per 100,000 individuals and assumed the epidemic was slowly declining with full school closures (Re = 0.9). For each scenario, we calculated the percentage of schools that would have at least one person arriving at school with an active COVID-19 infection on the first day of school; the percentage of in-person school days that would be lost due to scheduled distance learning, symptomatic screening or quarantine; the cumulative infection rate for students, staff and teachers over the first three months of school; and the effective reproduction number averaged over the first three months of school within the community. FindingsIn-person schooling poses significant risks to students, teachers, and staff. On the first day of school, 5-42% of schools would have at least one person arrive at school with active COVID-19, depending on the incidence of COVID in the community and the school type. However, reducing class sizes via A/B school scheduling, combined with an incremental approach that returns elementary schools in person and keeps all other students remote, can mitigate COVID transmission. In the absence of any countermeasures in schools, we expect 6 - 25% of teaching and non-teaching staff and 4 - 20% of students to be infected with COVID in the first three months of school, depending upon the case detection rate. Schools can lower this risk to as low as 0.2% for staff and 0.1% for students by returning elementary schools with a hybrid schedule while all other grades continue learning remotely. However, this approach would require 60-85% of all school days to be spent at home. Despite the significant risks to the school population, reopening schools would not significantly increase community-wide transmission, provided sufficient countermeasures are implemented in schools. InterpretationWithout extensive countermeasures, school reopening may lead to an increase in infections and a significant number of re-closures as cases are identified among staff and students. Returning elementary schools only with A/B scheduling is the lowest risk school reopening strategy that includes some in-person learning. Research in context Evidence before this studyScientific evidence on COVID-19 transmission has been evolving rapidly. We searched PubMed on 6 September 2020 for studies using the phrase ("COVID-19" OR "SARS-CoV-2") AND ("model" OR "modeling" OR "modelling") AND ("schools") AND ("interventions"). This returned 17 studies, of which 6 were retained after screening. A wide variety of impacts from school closures were reported: from 2-4% of deaths at the lower end to reducing peak numbers of infections by 40-60% at the upper end. Drivers of this variability include (a) different epidemic contexts when school closure scenarios are enacted, (b) different timeframes and endpoints, and (c) different model structures and parameterizations. Thus, considerable variation in predicted impacts of school closures has been reported. Added value of this studyTo our knowledge, this is the first modeling study that explores the trade-offs between increased risk of COVID-19 transmission and school days lost, taking into account detailed data on school demographics and contact patterns, a set of classroom countermeasures based on proposed policies, and applies them to range of community transmission levels. If rates of community transmission are high, school reopening will accelerate the epidemic, but will not change its overall course. However, even if rates of community transmission are low, complete school reopening risks returning to exponential epidemic growth. Staged school reopening coupled with aggressive countermeasures is the safest strategy, but even so, reactive school closures will likely be necessary to prevent epidemic spread. Implications of all the available evidenceThe impact of school reopening on the COVID-19 epidemic depends on the transmission context and specific countermeasures used, and no reopening strategies are zero risk. However, by layering multiple types of countermeasures and responding quickly to increases in new infections, the risks of school reopening can be minimized.

The role of masks in reducing the risk of new waves of COVID-19 in low transmission settings: a modeling study

ObjectivesTo evaluate the risk of a new wave of coronavirus disease 2019 (COVID-19) in a setting with ongoing low transmission, high mobility, and an effective test-and-trace system, under different assumptions about mask uptake. DesignWe used a stochastic agent-based microsimulation model to create multiple simulations of possible epidemic trajectories that could eventuate over a five-week period following prolonged low levels of community transmission. SettingWe calibrated the model to the epidemiological and policy environment in New South Wales, Australia, at the end of August 2020. ParticipantsNone InterventionFrom September 1, 2020, we ran the stochastic model with the same initial conditions(i.e., those prevailing at August 31, 2020), and analyzed the outputs of the model to determine the probability of exceeding a given number of new diagnoses and active cases within five weeks, under three assumptions about future mask usage: a baseline scenario of 30% uptake, a scenario assuming no mask usage, and a scenario assuming mandatory mask usage with near-universal uptake (95%). Main outcome measureProbability of exceeding a given number of new diagnoses and active cases within five weeks. ResultsThe policy environment at the end of August is sufficient to slow the rate of epidemic growth, but may not stop the epidemic from growing: we estimate a 20% chance that NSW will be diagnosing at least 50 new cases per day within five weeks from the date of this analysis. Mandatory mask usage would reduce this to 6-9%. ConclusionsMandating the use of masks in community settings would significantly reduce the risk of epidemic resurgence.

Controlling COVID-19 via test-trace-quarantine

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.

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

AimsWe 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. MethodsAn 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. ResultsPolicy 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. ConclusionsIn 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.

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.