How will mass-vaccination change COVID-19 lockdown requirements in Australia?

Background To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine. The rest of the population will instead have access to a less effective vaccine. Methods We apply a large-scale agent-based model of COVID-19 in Australia to investigate the possible implications of this hybrid approach to mass-vaccination. The model is calibrated to recent epidemiological and demographic data available in Australia, and accounts for several components of vaccine efficacy. Findings Within a feasible range of vaccine efficacy values, our model supports the assertion that complete herd immunity due to vaccination is not likely in the Australian context. For realistic scenarios in which herd immunity is not achieved, we simulate the effects of mass-vaccination on epidemic growth rate, and investigate the requirements of lockdown measures applied to curb subsequent outbreaks. In our simulations, Australia's vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43% and 52%, respectively. The severity of epidemics, as measured by the peak number of daily new cases, decreases by up to two orders of magnitude under plausible mass-vaccination and lockdown strategies. Interpretation The study presents a strong argument for a large-scale vaccination campaign in Australia, which would substantially reduce both the intensity of future outbreaks and the stringency of non-pharmaceutical interventions required for their suppression. Funding Australian Research Council; National Health and Medical Research Council.

Analysis of SARS-CoV-2 Temporal Molecular Networks Using Global and Local Topological Characteristics

The global COVID-19 pandemic continues to have a devastating impact on human population health. In an effort to fully characterize the virus, a significant volume of SARS-CoV-2 genomes have been collected from infected individuals and sequenced. Comprehensive application of this molecular data toward epidemiological analysis in large parts has employed methods arising from phylogenetics. While undeniably valuable, phylogenetic methods have their limitations. For instance, due to their rooted structure, outgroup samples are often needed to contextualize genetic relationships inferred by branching. In this paper we describe an alternative: global and local topological characterization of neighborhood graphs relating viral genomes collected from samples in longitudinal studies. The applicability of our approach is demonstrated by constructing and analyzing such graphs using two distinct datasets from Israel and France, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps

Environmental problems due to human activities such as deforestation, urbanisation, and large scale intensive farming are some of the major factors behind the rapid spread of many infectious diseases. This in turn poses significant challenges not only in as regards providing adequate healthcare, but also in supporting healthcare workers, medical researchers, policy makers, and others involved in managing infectious diseases. These challenges include surveillance, tracking of infections, communication of public health knowledge and promotion of behavioural change. Behind these challenges lies a complex set of factors which include not only biomedical and population health determinants but also environmental, climatic, geographic, and socioeconomic variables. While there is broad agreement that these factors are best understood when considered in conjunction, aggregating and presenting diverse information sources requires effective information systems, software tools, and data visualisation. In this article, we argue that interactive maps, which couple geographical information systems and advanced information visualisation techniques, provide a suitable unifying framework for coordinating these tasks. Therefore, we examine how interactive maps can support spatial epidemiological visualisation and modelling involving distributed and dynamic data sources and incorporating temporal aspects of disease spread. Combining spatial and temporal aspects can be crucial in such applications. We discuss these issues in the context of support for disease surveillance in remote regions, utilising tools that facilitate distributed data collection and enable multidisciplinary collaboration, while also providing support for simulation and data analysis. We show that interactive maps deployed on a combination of mobile devices and large screens can provide effective means for collection, sharing, and analysis of health data.

An algebraic framework for structured epidemic modelling.

Pandemic management requires that scientists rapidly formulate and analyse epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific modelling workflows detach the structure of a model-its submodels and their interactions-from its implementation in software. Consequently, incorporating local changes to model components may require global edits to the code base through a manual, time-intensive and error-prone process. We propose a compositional modelling framework that uses high-level algebraic structures to capture domain-specific scientific knowledge and bridge the gap between how scientists think about models and the code that implements them. These algebraic structures, grounded in applied category theory, simplify and expedite modelling tasks such as model specification, stratification, analysis and calibration. With their structure made explicit, models also become easier to communicate, criticize and refine in light of stakeholder feedback. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Hyper-differential sensitivity analysis for inverse problems governed by ODEs with application to COVID-19 modeling.

We consider inverse problems governed by systems of ordinary differential equations (ODEs) that contain uncertain parameters in addition to the parameters being estimated. In such problems, which are common in applications, it is important to understand the sensitivity of the solution of the inverse problem to the uncertain model parameters. It is also of interest to understand the sensitivity of the inverse problem solution to different types of measurements or parameters describing the experimental setup. Hyper-differential sensitivity analysis (HDSA) is a sensitivity analysis approach that provides tools for such tasks. We extend existing HDSA methods by developing methods for quantifying the uncertainty in the estimated parameters. Specifically, we propose a linear approximation to the solution of the inverse problem that allows efficiently approximating the statistical properties of the estimated parameters. We also explore the use of this linear model for approximate global sensitivity analysis. As a driving application, we consider an inverse problem governed by a COVID-19 model. We present comprehensive computational studies that examine the sensitivity of this inverse problem to several uncertain model parameters and different types of measurement data. Our results also demonstrate the effectiveness of the linear approximation model for uncertainty quantification in inverse problems and for parameter screening.

Using Agent-Based Simulator to Assess Interventions Against COVID-19 in a Small Community Generated from Map Data

During the COVID-19 pandemic, governments have struggled to devise strategies to slow down the spread of the virus. This struggle happens because pandemics are complex scenarios with many unknown variables. In this context, simulated models are used to evaluate strategies for mitigating this and future pandemics. This paper proposes a simulator that analyses small communities by using real geographical data to model the road interactions and the agent's behaviors. Our simulator consists of three different modules: Environment, Mobility, and Infection module. The environment module recreates an area based on map data, including houses, restaurants, and roads. The mobility module determines the agents' movement in the map based on their work schedule and needs, such as eating at restaurants, doing groceries, and going to work. The infection module simulates four cases of infection: on the road, at home, at a building, and off the map. We simulate the surrounding areas of the University of Tsukuba and design three intervention strategies, comparing them to a scenario without any intervention. The interventions are: 1) PCR testing and self-isolation if positive;2) applying lockdown measures to restaurants and barbershops 3) closing grocery stores and restaurants and providing delivery instead. For all scenarios, we observe two areas where most infection happens: hubs, where people from different occupations can meet (e.g., restaurants), and non-hubs, where people with the same occupation meet (e.g., offices). The simulations show that most interventions reduce the total number of infected agents by a large margin. We observed that interventions targeting hubs (2-4) did not impact the infection at non-hubs. In addition, the intervention targeting people's behavior (1) ended up creating a cluster at the testing center. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

Characterisation of Omicron Variant during COVID-19 Pandemic and the Impact of Vaccination, Transmission Rate, Mortality, and Reinfection in South Africa, Germany, and Brazil.

Several variants of SARS-CoV-2 have been identified in different parts of the world, including Gamma, detected in Brazil, Delta, detected in India, and the recent Omicron variant, detected in South Africa. The emergence of a new variant is a cause of great concern. This work considers an extended version of an SIRD model capable of incorporating the effects of vaccination, time-dependent transmissibility rates, mortality, and even potential reinfections during the pandemic. We use this model to characterise the Omicron wave in Brazil, South Africa, and Germany. During Omicron, the transmissibility increased by five for Brazil and Germany and eight for South Africa, whereas the estimated mortality was reduced by three-fold. We estimated that the reported cases accounted for less than 25% of the actual cases during Omicron. The mortality among the nonvaccinated population in these countries is, on average, three to four times higher than the mortality among the fully vaccinated. Finally, we could only reproduce the observed dynamics after introducing a new parameter that accounts for the percentage of the population that can be reinfected. Reinfection was as high as 40% in South Africa, which has only 29% of its population fully vaccinated and as low as 13% in Brazil, which has over 70% and 80% of its population fully vaccinated and with at least one dose, respectively. The calibrated models were able to estimate essential features of the complex virus and vaccination dynamics and stand as valuable tools for quantifying the impact of protocols and decisions in different populations.

Timing the race of vaccination, new variants, and relaxing restrictions during COVID-19 pandemic.

Late in 2019, China identified a new type of coronavirus, SARS-CoV-2, and due to its fast spread, the World Health Organisation (WHO) declared a pandemic named COVID-19. Some variants of this virus were detected, including the Delta, which caused new waves of infections. This work uses an extended version of a SIRD model that includes vaccination effects to measure the impact of the Delta variant in three countries: Germany, Israel and Brazil. The calibrated models were able to reproduce the dynamics of the above countries. In addition, hypothetical scenarios were simulated to quantify the impact of vaccination and mitigation policies during the Delta wave. The results showed that the model could reproduce the complex dynamics observed in the different countries. The estimated increase of transmission rate due to the Delta variant was highest in Israel (7.9), followed by Germany (2.7) and Brazil (1.5). These values may support the hypothesis that people immunised against COVID-19 may lose their defensive antibodies with time since Israel, Germany, and Brazil fully vaccinated half of the population in March, July, and October. The scenario to study the impact of vaccination revealed relative reductions in the total number of deaths between 30% and 250%; an absolute reduction of 300 thousand deaths in Brazil due to vaccination during the Delta wave. The second hypothetical scenario revealed that mitigation policies saved up to 300 thousand Brazilians; relative reductions in the total number of deaths between 24% and 120% in the three analysed countries. Therefore, the results suggest that both vaccination and mitigation policies were crucial in decreasing the spread and the number of deaths during the Delta wave.

Simulating Transmission Scenarios of the Delta Variant of SARS-CoV-2 in Australia.

An outbreak of the Delta (B.1.617.2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic. The ongoing epidemic is of major concern as the Delta variant is more infectious than previous variants that circulated in Australia in 2020. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, including case isolation, home quarantine, school closures, and stay-at-home restrictions (i.e., "social distancing.") Our modelling indicated that the levels of reduced interactions in workplaces and across communities attained in Sydney and other parts of the nation were inadequate for controlling the outbreak. A counter-factual analysis suggested that if 70% of the population followed tight stay-at-home restrictions, then at least 45 days would have been needed for new daily cases to fall from their peak to below ten per day. Our model predicted that, under a progressive vaccination rollout, if 40-50% of the Australian population follow stay-at-home restrictions, the incidence will peak by mid-October 2021: the peak in incidence across the nation was indeed observed in mid-October. We also quantified an expected burden on the healthcare system and potential fatalities across Australia.

Quantifying the Effects of Norms on COVID-19 Cases Using an Agent-Based Simulation

Modelling social phenomena in large-scale agent-based simulations has long been a challenge due to the computational cost of incorporating agents whose behaviors are determined by reasoning about their internal attitudes and external factors. However, COVID-19 has brought the urgency of doing this to the fore, as, in the absence of viable pharmaceutical interventions, the progression of the pandemic has primarily been driven by behaviors and behavioral interventions. In this paper, we address this problem by developing a large-scale data-driven agent-based simulation model where individual agents reason about their beliefs, objectives, trust in government, and the norms imposed by the government. These internal and external attitudes are based on actual data concerning daily activities of individuals, their political orientation, and norms being enforced in the US state of Virginia. Our model is calibrated using mobility and COVID-19 case data. We show the utility of our model by quantifying the benefits of the various behavioral interventions through counterfactual runs of our calibrated simulation. © 2022, Springer Nature Switzerland AG.

An Agent-Based Model of the Local Spread of SARS-CoV-2: Modeling Study.

BACKGROUND: The spread of SARS-CoV-2, originating in Wuhan, China, was classified as a pandemic by the World Health Organization on March 11, 2020. The governments of affected countries have implemented various measures to limit the spread of the virus. The starting point of this paper is the different government approaches, in terms of promulgating new legislative regulations to limit the virus diffusion and to contain negative effects on the populations. OBJECTIVE: This paper aims to study how the spread of SARS-CoV-2 is linked to government policies and to analyze how different policies have produced different results on public health. METHODS: Considering the official data provided by 4 countries (Italy, Germany, Sweden, and Brazil) and from the measures implemented by each government, we built an agent-based model to study the effects that these measures will have over time on different variables such as the total number of COVID-19 cases, intensive care unit (ICU) bed occupancy rates, and recovery and case-fatality rates. The model we implemented provides the possibility of modifying some starting variables, and it was thus possible to study the effects that some policies (eg, keeping the national borders closed or increasing the ICU beds) would have had on the spread of the infection. RESULTS: The 4 considered countries have adopted different containment measures for COVID-19, and the forecasts provided by the model for the considered variables have given different results. Italy and Germany seem to be able to limit the spread of the infection and any eventual second wave, while Sweden and Brazil do not seem to have the situation under control. This situation is also reflected in the forecasts of pressure on the National Health Services, which see Sweden and Brazil with a high occupancy rate of ICU beds in the coming months, with a consequent high number of deaths. CONCLUSIONS: In line with what we expected, the obtained results showed that the countries that have taken restrictive measures in terms of limiting the population mobility have managed more successfully than others to contain the spread of COVID-19. Moreover, the model demonstrated that herd immunity cannot be reached even in countries that have relied on a strategy without strict containment measures.

A Bayesian approach for monitoring epidemics in presence of undetected cases.

One of the key indicators used in tracking the evolution of an infectious disease is the reproduction number. This quantity is usually computed using the reported number of cases, but ignoring that many more individuals may be infected (e.g. asymptomatic carriers). We develop a Bayesian procedure to quantify the impact of undetected infectious cases on the determination of the effective reproduction number. Our approach is stochastic, data-driven and not relying on any compartmental model. It is applied to the COVID-19 outbreak in eight different countries and all Italian regions, showing that the effect of undetected cases leads to estimates of the effective reproduction numbers larger than those obtained only with the reported cases by factors ranging from two to ten.

Simulating COVID-19 in a university environment.

Residential colleges and universities face unique challenges in providing in-person instruction during the COVID-19 pandemic. Administrators are currently faced with decisions about whether to open during the pandemic and what modifications of their normal operations might be necessary to protect students, faculty and staff. There is little information, however, on what measures are likely to be most effective and whether existing interventions could contain the spread of an outbreak on campus. We develop a full-scale stochastic agent-based model to determine whether in-person instruction could safely continue during the pandemic and evaluate the necessity of various interventions. Simulation results indicate that large scale randomized testing, contact-tracing, and quarantining are important components of a successful strategy for containing campus outbreaks. High test specificity is critical for keeping the size of the quarantine population manageable. Moving the largest classes online is also crucial for controlling both the size of outbreaks and the number of students in quarantine. Increased residential exposure can significantly impact the size of an outbreak, but it is likely more important to control non-residential social exposure among students. Finally, necessarily high quarantine rates even in controlled outbreaks imply significant absenteeism, indicating a need to plan for remote instruction of quarantined students.

Coronavirus pandemic: A predictive analysis of the peak outbreak epidemic in South Africa, Turkey, and Brazil.

In this research, we are interested in predicting the epidemic peak outbreak of the Coronavirus in South Africa, Turkey, and Brazil. Until now, there is no known safe treatment, hence the immunity system of the individual has a crucial role in recovering from this contagious disease. In general, the aged individuals probably have the highest rate of mortality due to COVID-19. It is well known that this immunity system can be affected by the age of the individual, so it is wise to consider an age-structured SEIR system to model Coronavirus transmission. For the COVID-19 epidemic, the individuals in the incubation stage are capable of infecting the susceptible individuals. All the mentioned points are regarded in building the responsible predictive mathematical model. The investigated model allows us to predict the spread of COID-19 in South Africa, Turkey, and Brazil. The epidemic peak outbreak in these countries is considered, and the estimated time of the end of infection is regarded by the help of some numerical simulations. Further, the influence of the isolation of the infected persons on the spread of COVID-19 disease is investigated.